{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "fHLDsKNdFYk9",
    "outputId": "98dd3747-14ac-48f2-a6dd-fe37fe708f1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2.5.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "from nltk import ne_chunk, pos_tag, word_tokenize\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers import Dense, Activation \n",
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.layers import Activation\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Lambda\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "import pickle\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3R_OuChg-utc"
   },
   "outputs": [],
   "source": [
    "# loading image array\n",
    "file1 = open(\"image_lists2.pkl\", \"rb\")\n",
    "image_lists = pickle.load(file1)\n",
    "file1.close()\n",
    "\n",
    "# loading word character \n",
    "file2 = open(\"char.pkl\", \"rb\")\n",
    "char = pickle.load(file2)\n",
    "file2.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "b3pOjvH-Ib4W"
   },
   "outputs": [],
   "source": [
    "#function to extract feature extaction for given image\n",
    "vgg16_model = tf.keras.applications.resnet.ResNet101(weights='imagenet',include_top=False,input_shape=([96, 96, 3]))\n",
    "\n",
    "def restnet101():\n",
    "  model = tf.keras.Sequential()\n",
    "\n",
    "  model.add(vgg16_model)\n",
    "  model.add(Conv2D(1024, (1, 1), activation = 'relu', name='block6_conv1'))\n",
    "\n",
    "  model.add(UpSampling2D((2,2), interpolation = 'bilinear'))\n",
    "\n",
    "  model.add(keras.layers.Reshape([36, 1024]))\n",
    "  model.add(tf.keras.layers.Dense(256,activation='relu'))\n",
    "\n",
    "  return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 644
    },
    "id": "t8tMJzLXVlUg",
    "outputId": "668057be-0abd-4206-eaaa-b926156c7ae8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator = restnet101()\n",
    "\n",
    "keras.utils.plot_model(generator, 'generator.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_list = np.array([i[0] for i in lists]) # character tokenizing \n",
    "\n",
    "t = Tokenizer(filters='|', char_level=True, oov_token=True) \n",
    "t.fit_on_texts(v)\n",
    "values = list((t.texts_to_sequences(v)))\n",
    "\n",
    "for i in range(len(values)):\n",
    "  values[i] = [38] + values[i] + [39]  #adding '39' as 'END' index to each list\n",
    "\n",
    "sen_list = sequence.pad_sequences(values, maxlen=24,padding='post',truncating='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xfJjQM_u0ak-",
    "outputId": "05a542e9-2776-4eaa-b4e0-1889bc2f8e14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: ((96, 96, 3), (24,)), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices((image_list, sen_list))\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "L7NALJb3_zYl"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 20000\n",
    "BATCH_SIZE = 10\n",
    "def make_batches(ds):\n",
    "  return (\n",
    "      ds\n",
    "      .cache()\n",
    "      .shuffle(BUFFER_SIZE)\n",
    "      .batch(BATCH_SIZE)\n",
    "      .prefetch(tf.data.AUTOTUNE))\n",
    "\n",
    "\n",
    "\n",
    "train_batches = make_batches(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "KCgQcQIwX-I_",
    "outputId": "dcefaf8d-e72a-4213-dbf7-4658717b70ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2048, 512)\n"
     ]
    },
    {
     "data": {
      "image/png": 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dCuihlw62ToA+D+Z5kANTUD+YN71+XBoOh3he0R+VN1mPZ9a5cLuodxbiOP5F4Bdn8v6nmfU/O+e4Xwc+fSGFyNl3hDM4cQ9gTm/gKCdwWOz+OAeQh/9s/mngL1T9/O2yIk2dS5kD7gnUJ8DXlcl+aS8gD/k0recUfaruJzBPHcIk5j4P9pPwz0TFHwX6LC/ZR2ZayQuFLoCeqfgsTJPPz+2bB3eq3gNfxNIDf1q555dJuCVO8jJ17gVzFXse/hPYh5nSP0ypR154AOqhFx6r0I8D+lEKfR7w8/vMpo8D+bMAcUUCLuCc+XG3j5t97J3BiXoAMz2BeQ7gsB7AYfCfB/7TQF9R5Sm1L6vy9LY5Kl+dgbaawVuegvgs6PUZ6GuyPAF44lw0WZpAX54GfLr/rJJXcuBWpINAT9dTsEsJnCehlwDJyyv6YLItnKwT+lNAjwNvAuoc4KdgnuSncfPIDzKQp4BOoX1wXRwT5gHvhXMBHvpRpsjz++ThHfrhicF9HLQPA3ke2KeF9ZMC9exvBqXvdQDo8nReuu+8dTl3fD5fkWb2719MuS/qDeRnzT7WzuAwR3BUSOigkj/aCZzVAQjYHw9/WZWzEI+iJvsmPYI8xNUM7MoB6OuHgH8W+qoiT6l6LVnPK/p0/xT2qZqfDAznGqKcm6WTg72AeTAZTE3zoiAD/QT6AvSx702HZZK8zAmksM9BPq/W0zBM5AfZYGge8rOAF0CfhF1CL8rUearM4wTkGfi96IAqD73oxGA/Wq1PoH6S2Hl++1H5Z7XDQJ7Pnwfu00B8ennUtqQdKUmPWFeQFQlJlsVSkVB0Bb5y/u8Nl87gI2+HOYJ5vYHZUNC8MNBRTkDW9AMOIB/SSdP5cM4s/LN8Rc5UfwryCeDFUlfkGYcw4wAyqMvHQj8D/iFwn5+XxOYzqIciHOMHYjkH9ul6HuJx4B0E/YyajwNPqPgU5J6frechn1fxaXxd7BNmsfJUrUcz6/nt85R7GESnhvtJwH6aMEs+/6w2D9z5/DzM56Vlzg5yPQsRTmCdwlzRRFpSZBRNFm1AF0sp2Z5Cf7JdQdFUJEVG1lRkXUOWZWRdRU7y+Uo2IefsJk1eJPy42cfWGRwZDsqBfdYJzBsLSB2Aouongn8K8HmAT3sEiiJnIR5FPQh9XZUT6CsZ0POOIP1YuugJmIp8APZ5yJv5nkAO6lqalnJv8OYgr0rMKHpPhG7ygA+83PZEyY9dEaZJ4B7l4e65B8CeqnexTEI2c6AutoWZIo+8cArokRcdVPTRRNnnlboXHYS0Fx0OcRDpFOJHKfOTxMlPaodBOx9KOUp9H6Wo87DW5el8TZYPqO0U1IquiHqsKxnIFU1B0eWpbbKmomRAFsBOAZ3mi7SWQFxFVjVQNSTDQlI1JFUDVUfSNCRVT9Yn25FVYlklzqWRFbFU9Wyd/+FvnO0B5ExiMhPv42YfS2dwGkcwGxI6LMwz6whSByCr+gH1ryjyAQeQbVPlqbyj4D/Jm4Z/CnVNFttT8JvqtMI3kmmcqpJv4NIB6GtyEsYJvYmqH3tIUYAU+hNFH/gT4OeBnqQz0OfCOlHgE7reobAPXDEYG7peBv7QC0WsPQF9GnePvIjQDycqPucQ8oo9D/lJ+milngf8WcIwJ7Xjwijp+mz6OKjr8kHVnQf6LLBT1Z3l68oUzFPoT0Fb11A0FcXUDwe5biJpCbx1U8Bc00HVkHVTQFwT2+ZCXNGIFRUUAfFY0YhkhTCKCZJn6UcxYeK0g1xa5Me44wg/jPGjiHEQ4UcefnhBbyBLXA4gfxTtJI7gNE5AVnVxbJI3UfaHw17OtoGqiWOm4S8cgKUpU3mWrmT7pao/Bb+hKmhyovZz4BezgyZqP7+uzir90J9AP/Qnqj+cOAABfAH4KFH7U8BP1H/kBwLmuZBN4HiZso88P4N9qupDLyTMwT0Ffab2p5S/UPMC7LOwnw93L5oOxRw2wJqmUzst4E8SIz8O7LOKPA/0dFse6oouI8nyFLjlXDqFvWaqSIqEauqJGtemlHmar2gaSrqPqiEZ5jTMDVMo8gT0km4m0NZBUYgTcKNoxKouwJ5sD5EySE/DPCbIQdwLYnw/hXeMG4T4oc84tKfA7oURjhfiBRFeEDFK02GU5IWMg8k+YZAIiYt6A5mLm1r6rNnHzhkcNWh8EicglP4E/nkHoKjqlPJPQz3z4K/qE+Vv6UoO8iq6IlPQlQz46dJUZAF6RcJIlb4iZ+kU+FoC+TQtlmJdlcgpfA/J8xLwe5NwTpgAfeyKpeeKME6STh1A6HqJeh8TeULFT6A+DXjfDaaUfJhX9ZkDSPJzCj4F+3w1P1n6R8yUyafT9ZParCpPFXa6bWqQkvkQn15OA1zR03i2ciS8FV1BNSfhE8U0MhUu6wm4NRXVMsQ+hjENbd1EMkyhzNN8zRCgVrUppY2qi3xFI5IU/CjOPmEEQZIOopzaDgSM06U/TtbDPKTHeIGD4yVA9gWQHS+YC+jAF+kwjIiSZRjEufQE5FHgiToahUSBWIbJMo7CbFv2gl6an74TckEmId7a/zjax84ZpDZvoPgwR5CP+SupA0jy870AVROxUKHwp51CmicrMnoCeiOFvypjacoBB2CqSgb5PPA1WaagKehqAhflIPQ1RazLoS9g77lIgVD5hB5SIBxA7I+Jx44A/owDyEM/dD0B/UTVp6AP3TGB62VAD5wgg3zgBAdUfqrk/SiaAv009KfT89T7UQOrJ7HDQi9HAX4W5vmlnky5TeEuKxKqqU7BXUvWZV1BM1VkXZlA3DSmwK6ahlDlpp4BPA/1Sdo6CPVUgadpRceLIYxivDDGnwN0L4gZh2EO6g7j0M4A7wZhBmvHC4XizlS4WHe8kCCICPyQMIgJQ5E+CuRhEAh4hwLisyA/DuLpr42m+Wn6tByYFYlntssw0UfD5oWF5jkCWdWmHEEaCkp7AqkTyKv9FPaqpiTKPwGCpmTwL+iKCPnoCmVDOIGSqc4Ff0GbrBc0BV2RppS+rkycgByHSMFYqPuxOwG+P0bynUzlR65NlIf92CV0RhnoQ9fLQJ+B3/UIHJ/ADQi9KFmGM8uJok/BPgv4fAjnMLgfNsh6lOWhPjtgmoJbZp5SF/unIJ+FuaIpqJaKoslimQO4ZqoopoZq6ciahlY0RZxcU9GKlkib+gTWuolsFScAt4oC4KpBnKpw1RCKXBVg92PwwxgnignCBNhRTBCCH0WMfAHukR8xHoWZCh/5IY4X4HjjDNpOkpcCe+yFAtReNAXsVIkLmE/DOvKnQZ0H+FGgPgukD0zTnmmjsqqjyPKUKMsLtXxvXdW1+T1yTUnarZwJuV9+/W+fqHzHlB75Av7c5lm0j5UzyNtxjmBeBUudQL6C5Z1AGvNXNZFvJE6gbGoC/Iaaqf+SqWYOwErAX9AUCpqMoYh14QAQg8WKUP26IifwdwX4/cQJ+C5SMBZK37GFondsYlekY8cmHAvg+7aThXTEcpw4AR/fDQicYAr2qcIXqn+i6iefo4Gf5sPplPxx6n0W9LNKfQr0xrQiF0uh1jPYF3VUU0cxjWQp1vOAz6BuFkXYJVlKZlFAXTOI1SRmrpnEqkGkGowTVZ5+/Gw9wh1HjIYC6CPfZhwMBOzDiKEb4CQQH3khw3EwBfbAD6egHiSD5UEK/BTsOaBHgTcX6FH6nsYMzGFaeR/XrvLtKwPzKduYoqo5YB8UWrNtTLQrFUtTKCcCy9IVirp6oI3lxZaqkAgBiWt/4vQcOfD9L3sGHx2b1x2cV0lFhZzjCDQFVVcylZFWUM1Qs3XhANQDlbNkivWCpkxVzpKewl/OFL+uJCBTJFQiAX8vgX8wWWagd21ixyZyR2LpuQS2OwV+33azsE6q9j3bF9BPHEDWA3CCDPpOeLjS9w9R+KcB/izs8wOmmnQ44PVkymyq5lOwq8lyaj2BeQp2rWiimgZq0USxChnQJ7AvIFtFYs3KAB+rBiSAD2VNDEqmgM/BfjROwR4y8m1GfgL3IMzgPnADHD9k6PoTxT4OCIMIfxxmEA/8vHIPM6Uejp25UI+y3zc6u0I/TJlPesgnayep4k7bRfbJtZOCrlAytSPbSSqSzKyXfFQ7mW4fkj8Q4VBnQGRPhFHkjojHDpE7IrBdAnd8WpQccf8uncGZLPmvz9eBzTiOf2/yk60/BzSBrwF/OI5jT5IkA/h7wOeBFvAH4zh+cOLrzI4RyApKfv6/qmWVOa3cim4hazqqbkypFN1QMqegJemSqWYOoGSIdMkU6bRSl5NxgrKuYmqiQotB4UmF1uIgAf9oUrE9h3icQH40mCh+xyYYOQL4IxffFp8gcQCe7WeQ94ZeBvtU4bthCvtoSt17UQr5+YO1cDTk84BPQzHZVEZpPtSttFFn4RkFvaRNgV0raqhWGpYx0AomWtFEK1oTqFtF5EJZLM0icrE8AbpWmIBds/BjcIOJQh+nMPdDhl4oYO4INW77IUN3xNDtT4F84AaMvRBvHGTg9scBgZco9QTqoedkyjzwnJnQS6Lc58D7pOCe7enKqo46A21Vt0S9NqwDwNYMJVHck/ptJKKmZGpZnbZ0hWpBSwA9+ZR0JQtnGoqUAdtUJbQ8pD0ngzVjm9jpEaVCZjQg7tqEzigTMr7t4o8mddq3PcZOwNAN8IcHe7KeH06JFyec1ONJvZ4emxL1+WJmE0nJdOyPo30YPYP/BngXqCTr/yvwl+I4/jlJkv4W8MeAv5ksO3EcPy9J0o8k+/3B01xottGkebOOQMk1GlXXphSNZqhTDaZc0DJ1U7M04QBMlUriBFJnkDYWQ512AKYqo4Rj0VicEZLvCFXj2UT2QCh+uy8aSg7+3mCmwdjjTOnPNpLACXDDKIH+tNJPQzp5tX8S6M+bHpnNkpHmg95S5EzJa0V9AvmShl4U4NeKxhTg07RWKiIVK8hmCvwKklUEo0isWwL4ekGodsXATcYvxkEsnF4YY7shw37IyPcYjkeM/JD+WIBl6AYMxgG9kSfCMW6APw7wEpU+C/jA8yfq3HOEQs/CMP5kEPQUcJ96TyUJrwhhok1i5YY1ESuGhaJbGdRF3RRhFN1Qp4RKraBj6cpUHS1ndVOloE1ESlo/TSV5iVGRkH0XyR8l41AjZM8h9hxRNzuirsajPpFjE4xcvL6Nb7s4tos/Suuohzf054oTfxweWj8Pg3ge5ofV0Ykoma6nxeRXcS1FTuop0DkNTY647qUzOL1JknQV+D3A/wL8t5KYoPs7mPyJw98F/izCGfxwkgb4BeCvSZIkzfyO94ktVU/prKG8I0iVk2boB9SSZqgYloqhK9QKWqaaagWNakGjmDiAkq5S1kVDszQZU02ngwrlpEWecAAje9K43CGR3SdMG5Y9IHRGeH17LvzHfS9rWL49cQCpOnLC6EDD8g9R+yeB/uwAbAr9CewnwFctVQDeEopeT+CvF4Wy18tF1KKJXilMw75QRi5WRJimVBOgV00io0isFYg0MxksnYB+HMQMRwG9dsDItxl6fQaeAHxv5NN1/EzFD0Y+gR/ijQXg/bGIs4u8IFPwoecIBZ8DfKre8zNc4PCwyyzcFU2fEh9TYE/qnaprBwSHbol1y1SzOpdC/aR1zlJllMhH8myhznNAjwYdAXPXTurfkMB28QY2/f4oq3PewBX1y/YzmKe9z3yd8yIysJ8F5IfVuRTiWS9yTp1TTTXrUepFDa2kz61zermIWrAmAqNY4b/4Lb//LCiZfuZIl87gjPaXgf8eKCfrTaAbx3GQrG8g/hgacn8QnfzxQy/Zfz9/QkmSfhz4cQC0osibM400nT46CQVZWYPUTDMDv25MHIBmiMZXK+iZA2iUdMpJQ6waKmVDzBSyVBlTkbA0WTTEcIzkDZBsB3k8RBoPiewB0bBLaPeJ7D5Bv483sPH6I7zBCL9vJ9AfZw0whb4/9LGDtAHGBxpfPsZ/XMPLz6xJG6ApT9S8aHAirRlioFUvaRnkjYqOVjTQK0X0ckE0tkoRrSLUu1yqoZRrSMUKmGVi3SLSi8RGiUgzcfyIYRAzDiMcPxYg90J6I59hN2TgBfRGQ1rDNsNEvXdHPuNxwNgJMqh744DAC/HHIqaeAj1V8PJqqyoAACAASURBVCnQw6npivPVej5mnge5ppsolYUz1ZmqpVE21VPWGRt5bIuw4XhINNglGg2I7D5Rv0+wcXidGeTqTArti6wzs9Nt83WmoStZndELWgbpfJ0xKhZq0TpZnTGKRJpFbJQIVRM3iHCCGCcQYiCrM67P0BN1pj0U9eRAnRkG+K2DdeYiTJJAv/w5itOZJEm/F9iN4/hrkiR930WdN/lD6S8CyIWFrDofNkUt7YrnG7Vo0KKbbVgamqFQsLRE/es0izrVgkbd0qiYGmVdoW5plHUVQ5UoajJW0s2WPRupPxBKbGwL+A+7RINu5gDG3UHWmMfdgYiL9oTy92wvC/24YcQwiKZU/3QjProhz3sZSpMOAr+kTkPfqBjoxWRZsdDLRYx6Cb1cRK+VkMt1oejLNeRyfdJ4jSKxXsSTdRxfqPmRL+att7s+Pddh6A3pjf2s4fYcn9ZwzNANGDs+nhPgjYNsGfghgTM8APrQc7OwzGS64+GQTwc8FS0d/NQSZZ4HfAHNUDPA65aGYakUMqgb1CzRI6wX9KweVE01C71YqlDmBU1G9kZCCHg28rhNPOoRtpO6MOhkYmDcH9HvDglsB7c7whv6mSBIQy2OF2Z1IA92d85b2EfVhVnVXdem60NJldE1RTz7BOZmxcCoGGhFA6NWRi2aGLUSRr2MUiwjl0Q9kMs1sCqiLiTO31cMxmGM7Uc4QUTPj+i5AQMvpO/69MYBPcenPfBobXsMXZ/WcMjY6eCPQ8aOj5/06tKeXOAMs3EYUR8mTv+oupDngqzqZ8HNwfNJ4j2fj6M9yZ7B9wI/JEnS7wZMxJjBXwFqkiSpSe8g/yfQ6R9Eb0iSpAJVxEDyqS3rqmuzXXQjcwS6pWKYovGXCxrNkkHV0miWdBZKxlTDLxsqRU2eNH4CpPFAKLqxDSPR4L1ei3jUx+90Mgcw7g4Yd4d4to/bcUWjH4pG77pBBv9hMGns0/P4Tw5+Uxbx0ZKaa+yGGKA1KgZGRUcv6lh1cwr6Rq2MXp3AXq40kEs1AXujTGRWCFWTvi/mvTtBxGAc0hsG9FybjtOl4/j0Rj4t26M9HNMd+YxsL2vgKez9sXcA9qHnTin6tIHPe67Z5IAkBKPo5iTsZ5ZQrRKqbqBbWubwzYKOZihUS/rUc24UdaqGSsXUaFgHn7MhRcLZuwNktwPugKjXItroEA27BP0ebquH2x/R6w6Es+8L1Z46+9nnnIL9tM85D3VTllnQp4FeVBW0xLGnzzlV50atnDl3o1GdPOdSDbnSOPCc7dxz3vMiemNfPN9xQNcRjr217tEbebSGDq7TZ+wET+Q5T8K7pnjGxSqaWTrgzA1LxbA0dFOlWtKpFcTzbZaEsKsaGv/Z5//qWXAyXTYuxwxObXEc/xngzwAkPYP/Lo7j/1SSpH8M/H7EjKIfA/5ZcsiXkvXfSLb/69OMF0y/QaxlsysU3RIVyNAxEuWnWxpmQcvg0CzqLFYM6pboDSwU9ET9KZT0xAHEHrLTQRrayE6PqNci7LWI+q0M/m5LKD9nf5CA38vgkAJhGAgYDINoSvWnSm/WxCCYNAWCicKXqGoKekFLGr5QdFbdxKgXMWplzGZFqLtaA7nSRKk2BQCsKpFZJjbKOLFCxxeq3vZD2o7Pvu3Tb/ns22N2+xu0bY/WcMwgme3h2h6eEzB2fXzXJXCG+O4wC9+Ennukcptt8KpuoRUqmXpXE7CLZ6ahWypmQaSbJZ3FsimWFYOGKXpwCwWNSg7oRU1G9YZITg95PAS7Q9jbFEDfbzF+r4ebOGu31WPcH7OXOGwBdA87CKee20kgnnfUk+c2eWYrpkZRVTCqk2dm1k3MmoVRL2PUSljNqnDQ1SZyVTw3CjUiq0pslgmNEkMvSp5ZRNf16bkBjx2fvZEngD30aNtjWkOPke3h7vp4D4NEfQd49jaBe28K1uk4ymEhttkxEkU3cz0t8cw006RYKaAvVzAsDbOoUbA0liomS2WDRklnoagnYks8t2IyvbSoSVhSKMSW00N2B0T9tmhrwy5hr4Xb2mTcGeK2e4w7NuP+GCd7bh6O7U2JrF4Ys39Bs4mQLscMLtL+FPBzkiT9eeAbwN9J8v8O8H9JknQPaCP+CPpElv8BuuxNYk2fmjGUqkTd0iiUdGolfapyLhb0zBFUTIWiKgtHgI/kDJDdHvJIVEavs0fU2cXrDRjtdgRIukPcjptVSrfj4roBPX869OMeA5K80rcUeQoipeTFNqtuohU1CguFDPxmo4rZrGA2qwL41SZKfQmKdSKzQlSo4ykGth8x9CKGXki77bM/GtAaeewNxuz2x7RtsXRtH3c0Ab8/9vDtXgb8wLWP7apnP+WRhWfMKWjohSJmQcewVMyijlnQsufSLOqs1EwapnbguZQNBTP2EsWePJfOHuF6i6izy7jdw2n12Ekgnz6XtGc2+1zsMDoW8LPPpaLKc59LCnajomMtlDEbVYx6CWuxjlJfQi7XJs/FqhJZtey5DMYRrXFIx/XZH3n03ICdvsvu4zHtu2N2+0Ncu4M78nBtP4H62Z/LZFxEPBervpLBPP9cdFOlUTGy9rJYMVgo6NTN5LkYAuQlXc6ei+J0wO4K0dTZJRp0Ge+3ce73Muc72ncY98e0Oy5bF/RczKRnXFLFuMa1qoGW9JL+5y+/f1KkHM4aQJEvZsxAkqQfRERLFOBn4jj+6ZntfwT435hET/5aHMc/k2z7MeB/TPL/fBzHf/e85flQnEEcx78C/EqSvg98z5x9XOA/Oes1JDkdK5CzHoKiT7qSelKxCyWdhYrBYtlkqWKwXDFZKIhK3ShoVJLeQFGVkJ0O8qiD7A4IW9t4rS3CXgtnr4Oz22XcHeC0hlmldjou44FHzw/p+ZMKnR/Im6ci9dzAXAr/qiY+pqli1k0KCwWMik5xqYzZFOC3luqo9UUB/uYqcakhuvuFOqNYoe8J8Hcdn509h9aox77tsdV12e45tIYeztDDtRO4uD7eyJ6CS+i5BEncfhYseZWoGhayqiUwKaGaJYxyBc1QKZT0DPYLFYOVqiXufdmgWdDFvbc0yoZMSZMpyqFQ824PadgWMLm/S9DZw2316O922W31GLVs3I7LaH+UKcIUJsNAON7T3PtKApGqJpZWUcesm1h1k8KChVkrYDarWEs1zGYVtb6I0lxFrjaJSw2iQp3IrDIKJYYJ3DdGHm3HpzXy2BmM2d0ds33XYb/fxx21GA3GuCMfz/Hx7AH+qEeQxMmP6l3N3vsU6IXmFbRi9YCjtUo6S0m9X62ZLBR1lkoGCwWNmqlR0RPxI4Wizo864PQJW1tEvRb+/g7Ogw7j7pDRbgenZeN0XPodl92c8BH1PpoD8qPvfTqLqKrJXC9oGGURzkzvv9UsZfW+sLyQiR2luUJkijofWXXsIKY/Dhn6Ee2Rz47t0XF89odj+J0vnRUv02W/gJ5B8v7VXwe+HzGR5quSJH0pjuN3Znb9+TiOf2Lm2Abwk8BriH92/lpy7Lkmz37k30DOzySSc/OzVbOEXixjFnTRTa0YlMsGqzWL1arJSs1kqWiwWjJoWBpVI1GcoYNsd1BGHcLWY4LWNmFnl9F2C3u7jdvqYe/YjPZHuF0Xpzum7YUZgOzcdM9ZJZOGfOYpmFJJqJfCQoHCgkVhqYK1WKewVMdYEtBRmitQbhIWm0RWnb4X0RtH9MchW8Mxu9tjdgdjNtofsDdw2e26jPoCNgL4I3y7hzfqZbDJgz5v6b1UDQuzuoBiWOiFKnq5gWFpWCWdQtnAKums1kyu1gus1EyWS0I1Lpd0qoYi4C75yHYb2ekRd7YJ9jaJ9ncZvb2Ps9sV93R3wPrOKFPw/cSh9vwwg3r6gtHsPZ2FSkmVuVXUMrVuLVgUFgoUl8q5e7og7ml9Eam2TFhsEltVhpFCbxwy9CK2BmMejzy2h2M22g7bPYedrsto6OFsJ70n28W33517T6PAm3tPFU1HNYtJ76iKVqxiWAbla0tYpWsUSjrLNZPFssnVhsVK2WSpmLunukJZjVHsloD2YJ9gd5Ows8t4dwN7+5vinj4aiHracRntO2LWTQLsx2HE3UMc5TyBUtUUqppMRVMScWJh1U0azzcpLNWxlmoUlhdQmisozVWk2hJRUThIBy2pqyF7tseu7bEzHLPZdtgZjNnqOgz7Y1zbY5SIk7Se+naPcMsleJDW0z5x1AHuZu0/7xDz91S3LAplA7OgYZUvZgBZlqSLmk30PcC9RBwjSdLPIabXzzqDefa7gF+K47idHPtLwA8C//A8BfrIOwPITyeVJ11fwxKDS6YiegRFEX5YrZpcaxRYKuqslg2aiSOo6DKy00EZ7BF3d/Bb24R7m9iPd3F2u9jbLeEEWo5oWMNJD6AfHK7+8y++FHOKv66rQm3WTYrLRUqrVcxGldLVRfRmE2VxDXX5ulA8xSaOWmQwDumMQ3ZaHrvDFpt9dwKpjoOdhHec4ZjxoI8/6uHZPQLXPhJQYtDVQjWLaFZJAL9YwizqFCsGxYrBcgL8tYbFckk40eWSmGFTNWT0cQ951EHqPSLc3ibY3WC8u89ot0Nru8Vwq89o32HUGjHsuBmU8rA/DEop5K+YCiVVEs6zbmLWTErLRQpLJQpLdQorDazVZZTmKurSmnCcpUUCs0bfC+m6Ifdtn117zNZwzKP9EVsbDo/bfez+Hu7Iwxl4OIOBgFF675KY+qw6zztM1SxilBtJj6iGWdApVMW9q5UNrtYL3FgosFQyWCkZXKkYVHSFmpkIkFEHZbhHsLcpeqE7W4zudBhttxhu9Rjtj2jvO2zsO/T9MBMg9sy7Jvl7p0iTGWT5une9IepdaalAcbmItVSnuNKgsCJ6mOriGlJjlahQJyw2GQQSvXHI/shn1/Z4fzDmcddhq+ey0R7R67q4ex72++PEOT5iPHzjSMGRh3jeMerlBmZRp7G8RqFym1rVZLVmcbVucaVqslI2WS0ZVE2FuqlQkkNku4Vst4nbWwQtITbszXcY7XZxHncZ7ooe5G+7IN7k/23uGFuQJOn13PoXk9mQkJtKn9gG8IU55/iPJUn6D4D3gD8Zx/H6IceuzTn2VPaxcQbpyz2qbqFaJcxiAaukU6yY1OsmN5pFrjYsrtcs1iomi0WdhqVQ00CxWyitXcLWY7yddcaPN7G3Wgw39xhuDbB3bewdm47t0/Mj2l44Ff7JA0zM5pEyhdrQFRq6QqFhUVwuUF4tUVxtUFpboHhlSUD/yi3iyhJRsclIKbDthrSdkEc9h92Wx4P9XR62bLbaDsOey6g3ZjQc4w3ajIftZPDWzqZh5u9L2uCMch2j3EArVDErVYpVA6tkUG9YXG0UuNEscLVmsVIyuFoxaVgKVUNBtfdRhvtC0W+9zviOuDf25h7DrT57Ozb27ojeYDxzbwTgZ+9NOtupqMg0dJnrBYNS3aS4VKR8pURhqUJpbZHi2iLa8jXUletQXSIsL+NoZXrjkLYTsNEf83Dg8qA1Evem6zJoO9gPxokzfJ/x8KtJyMue+lXO2XujmsUktFLFrNaprzSxSquUamZ2b240C6yWDNYqJg1ToWoqGOMeSn+HuLtDsLOOv73OaPseg0c72BsDhl8ZMty1GfXH7I2FcPggjHgrEQ/zRIOlSMm9EfWmXDUoLRUpLFgsfmKZ4toixZUm+upV1OXrSPUVwsoSnlmnN45ouwEbPZdd2+Nhe8RGx+GNfZt+0ku0+y5ur5Op7uB9m+AdhygYEkc94D5A1sPWElAbpQZmtYlVEg6uVDNZrpl814061z5XYLVscL1qUjdVaqaCFdjIgx2k/h7B4w8IW9sM17cZbu5hb3cZPB5i79o43TF744C2F2VtKnVu+XvTkSVcRWZflVnXFBq6TLVsUFwqUFgoULlapbDSpHR1EWN1jebLn2ehsUpYFu2q70VQK52fNaf7OYr9OI5fO8fl/h/gH8ZxPJYk6Y8jXtL9Hec435H2sXAGwGQaWjqDyFCzmQyrNYurDYu1qnAEV8oGNVOhIvso3W2k/i7+4w8Idh4xXN9h8GiH4VaPweMhw60h/ZGfNWY7mQk0W1nT+fsp5BYNlULDonylROVqmeJKk/L1Zcxr11AX15CXbxJWlgisBjtOwJ4dsPV4zHpvl0etEff3hjzes5MGPGbU6wv4D9pTSj8PN0W30ItVtEIFvVjFqC5SrBQoVA1KVZMbSyWu1i1uLxa5XrVYLRk0C4lDHO6h9DcJdh4RvPkAe2OLnc19hpst+usDRq0Rvbab3IdwqjeUQi2v5Bd0hYau09AVqg2T0mqJ0nKR0lqT8vUlCqvLqFduoq7eJiwtEJaXabkRXTfkXt9lc+Byf8/m/teHbLU7DHtbDLsuzsBh3NvL7oPvDI+EvFldpHr9E1jlIsWKQblhsVy3uL1Y4uZCkbWKyfWqAHzDUlHtfdT+NuHeBsHWW7hbOwze2mG4uc9ga8ju4yHvt0a0vZC2F9EPwgOOL1XlaX2oajKLhsL1hkVxqUD5Sony1TqltUVK15ZRV2+hrlwnql0hKi/RDxVaTsDO0ONRz+U3WzYbHYf7u0Ph8N4eY/+Gy7j3Ad7oDXy7j59M45ztuaRA18sNAfRKlcWbVyhWbrPQFPfhxkKB61WL61WLhiXug+X1UIZ7xLuPCPY2GW8+YvDoq9hbLQbv9BhsDbF3RuyPA9peyNeDiH87x8mlbxGL+6CwaChUTZXicpGFl5oUlkq8dn2Z8vVltJVraFduEjeuEpYWsSWTlhOyNxL34XHf5e7OkIf7Nr2uy7DrMuw6jAddxo/beN/uEHg24fibRMFXp+7DRb1nABc2tTSdSp9afpo9AHEc56fW/wzwF3PHft/Msb9y3gJ95J3BVIgoqfx6Mu84HTRbrZqJIzBYKes0LYVi7KJ2Nol3H+HvPMJ5+JDh5h69D7bpb4jeQLvlZE4gbfR5J5Cq3Iqaq+gJ+Go3qhRXmlRurWJev4m6fB2WbhBWVumjszcK2GiNedTb5f6ezd2dATsdh97+iFF/zKjbxe3vZco2cIYHgKcXq+ilOnqxillfoVgxKdVMqg2LGwtFXl4tc6MmGvlSUWOxMIFdsHmPYPsRw4ebbDzYYrjVE997x2Z/6LE3FsAfzjTwFPglVeaapVHVZJZNjdKVEuXVEpWrVUrXl6ncXEW7+jzq8jXC2hpBZYW2E7I38nmj47A5cLm7PeTuWwN63fv022+Lhp2A3h/154Je0a0klFWn0FzDqjUo1cT3XmoWeHG5zM2FIjdrFlcrJs2CQl0JUAa7SJ1N/I17+Nvr9F/fov9gSwB+fcD7rRF7YxF+SR2dE04/61nV/ulli/JqidKVEpXrC1RurlC4egV17TnkxeuE1RVGWoW2G7I99HjQcXij63B3Z8j97QHDnsvwKy52p4c3+Drjwb/CG/UP9PCyel2oYJQb6OUGxXqVhbXnMif/wnKJW40CN+sWKyWdhqlQCIYogx2i7Q8INt9ntPE2g/Vdend3GT4eMtga0u6LMa+3vJAvz1HlaZipkvRyFw2FcrNA5VqZlc8u87mbq1RurQqQX32OuL5GWF6iE6q0RiEbfZcHXYcH+zbf3Bmw2xolEHdxum3GO228ex18p03obRJHvzZVxzWrhJZ8b6O6SKlmUWlYVGsmr720yAsrJdbKJrfqFosFjYaloPa3Ubqbore2cY/h+g7DzV2e//IF8ObiXjr7KvBC8sOdm4jZkz+a30GSpNU4jreS1R9C/MYbwL8A/oIkSfVk/QdIpvGfxz7yzgAmv0Ok6hZ6oYhV1kUXf6HAC8tlXlwq8UKjyGJRZclSULsbAgwP3sW+f5/B+i7de8IJ9NcH7Lg+bS+i44dTvYB0ALiqKdQT+DfrJrUbVSpXy1SfW6N8fRnj1suoa88T1K4w0ms8HAVs9sfc3xzxzusb3N8bsrNr098fYfdHjFqbeIP2AejnIVBevolZX6FUK1JdKLC4UOCV1Qovr5S5VhXqdrmoYoxaqN0N/I238T64S+/9TQaPdti83+XdjQHt/phtN5j6binzUsin3+2lmknlapnazSrl64vUnlvDvPkc2tXniJo38Cur7DsBjwced1sjPmjZ3Nnqs7ln03trxOBXHZzOm7i9X56COxwEu1VfyQDXWHyV20slXl4t82KzmDgzlWo8QulvET1+H//Rewwfvknv/U06X2vTXx/Q3R+x7QbseCF35zgx4bgFyFdMhdpqicrVCrd+53NUn1ujeP0q2rUXYeU2YWWVdqix7wTca42417K5v2fz+uM+3T2bYdfFbrdw+3t4ux38h0PCX24h3pN8KwOZXhLhuUJzmXJdgOzWaoXf9ZlVbjYKvNAoslLSaJoyancDdgW47fv36T/YpvfBLt2HfYb3h+y4/pSTztfLjiLjqzJ7WvLdatP1svrcGuXPfJ7a73mRK/WrjNQSO6MAqeMy6Dp0twfc2erTaTv090cM2l3c3h7eoI1n96d6HbKqowwt9M0K5nARc1MIkepCxMpSmxeXfV5eKXO7XuC5hsVvuVZO6mUf/8EDvMcP6b2/Sbf7mH53QH9jwF5P9DrTepl3xOmLdRVV1Mvlsi4E180q1ZvLQnDdfA795suE1Sv4lVU25CY7xivcLf5WPliwuXtlAPz8uVkjcTEDyMlP7vwEAuwK8LNxHL8tSdJPAa/Hcfwl4L+WJOmHgAAx3f6PJMe2JUn6cwiHAvBT6WDyeUw64+/APRMmFxZi49N/cAKU2gqlhQaVusXSSolPrVV5YVk4gutVg6WiitZZh+338dffY/De+wImH7TpPezRbjlsuyH7XjBVIdMwUKqO1iw1A0njpRXqL17DvH4T/fYnCWtruKVltocB632Xb+/bvLnR4/7ukNbOkN7+CKfbxuls4w07BwApBtIqmNVFCs1Vyg2L2mKR51YrvLxa5qXFEs83CiwWVKphH7X9CH/9LuMPvk33vXV6D3bo3O/SXx+IF5DGIf3kxal50F8xFRarAvqNFxpUbq5Se+E62u1PIq/cIqhfZ99X2Rv53Nm3eXd7wN2dAQ8fD+jtjxh2bUatTca9/QOhihT2RnUhg321WWBlpcSLy2U+eaXCSwtFVks6ywUFtf0Adh/iP3yX4QeP6L63Tvtei/5Gn719Z8qJzXs2i4bKmqVSXilSv12jcr1J/cXrFF54CfXq80TNG9jWAnujkPsdh7ttmzuPB7y72aO7Z9NvO4zaezid7QPOK3XMRqmOWV3Eqi9RaRaoLRa5vVLm1WtVXmwWuVGzWCmqlMKheDYP3mH88B7d99bpvL9N72GP7qM+m06Q9EIOfzaLhsJKzaR6o0LtRpXai9eovngL7erzSGsvENavs+/JPB543Nm3ubc35FubPR5vi3DSoN3H6Wwz7u3j2b2pXlYaUkwdcblRo7ZUZHGhwKfWqnxipcztRoGrZZ0FS0HtPIKte/jr79F/7z7de5t0P+jQfdhjt+1kIO/5s+Jp8mxWTIX61QrVq2XqL61Sf/EaxrVb6Lc/SdC4jmPU2Rr63Gs7POiOeHO9x93HffqtEb3WCKezL77PsDPVe0rbTfpsCs1lqs0ClWaBF66IdnOrXuD3ffrK184Zw+fKi5+K/8u/+gsn2venfvCVc1/vw7SPhTMwynX0coNCc43aUplqs8BL12q8eq3KC40izzUsrpQ0tN4m0tZdvHtvMvhgnfa7D2nfFRV6MwmNdPxwSlGm0z+XDZVrBY3KtTKN5+vUX7xK7cVr6Lc/hXTtFYLaVXacmEe9Md/eH/LGek8orZ0h3T2bUWsLt7fHeNDJgDmJ81ew6iuY9RVqi0L5v3Ktxmev1XiuUeB23eRKURXK8fF7ePfepHv3Ed33Ntj/disD/7YbZCGOMJ5ujCumylpJp3K1TPOFBvVXrlO9fRX9+Vfhyov49WtsDX3We2Pe2Rvy5kaP9x73aSfldzvboiEOOhlU0vIbCVCs+hK1xSK1xSKvrFX57LUqLzSL3KyZLFsSaucR8ca38e5/S4D+zjqtux36mwM2nYCdcZBN0YVJ+VOndaViCAf8Qp3GKzcoP3cD/flXiVdewK+ssjnwudce8X5nxDcedrm/PaCzO6S7O0jKv4Nn9wg9B2Cq/IXmGlZ9gdpikaXVMi+vVvjM1Sq36wVu1QwW9RClu0G0fofx3bdE+d97TPtum+7WkE0nYN8T4yl5R5Uq2hVTYa1uZU638fINys/fRH/+VaKl53BLyzwe+txtOby3P+TN9R4fbPVp79j093tTUM+XP4WgVV+h0FiktlRkZbXMK6sVPr1W5YVmgWsVgwVljNpZJ3z0Lu69d3Ll79DesZPyHxRBE9GgstoU5V94aZH6Kzco3r6N/vyrhAu3cKwmmwOfb+/b3GvZfONhJxMM/f3OAacEEwdrVhKx0GhSXy6xslLileT+v7wgek4NHJTOBsH9N3Hu3aH7/iadb2/Rutuhuz/i0cg/4IxmHesfbd85N5zXXvxU/Mf/+j850b4/+QMvf6ScwUc7TCRJk9iiWcIsmhQrJmuLIl7+8kJJgLQgo+3cIXjwNqN332L/zXu077Vo322z3puETdJGoMsSDV04gOsFjdr1CoufWKD5yVuUX34R4+XPEyw+TxuL+x2Xt9aHvPHr93jnYYfu3ojOditTy57dy9SYapYwE5VcWVmhsVzixpUyr91q8NnVCjdrJmsWqO0HBO+/yejrb9G+84C9d3Z4906Lbdtj0zlY1qomyvrZmpmVtfHKDSqfeFmUtXmTllTmg67LnX2bf/CgwzsPO7S/ZtP7f9vYu/8Ib9g5UNYUkpWVFVZuvsKtq1/gC881eXmhxItNiytpWe9+g+E7b9G9+6vs/toWvYd9HrUdHrsBbx9S1jVLZeFWjZXPLvPKH3iN6qc+gf5SUlaKbPQ9vrnd52uPunxto8f+4wG93ba4r+/v473ZI47eQZK/FsHA9gAAIABJREFUPQX0yopwqM9fr/K7P3eFV1cqvNi0WC2qaLvvEbz/TZx7d9h/8x577+zQvd9l/c7BOhDIEvc0ma6usl8QZW2+0KD5qZtUP/UJ1v6972d56QX24iLr/TGtnQGPH3R4b7NHa2tIf7+Hvfdoqg5IsoLiWRiP6hSd65TWF2jekbh9dZ9XrwV87kqVTy8V+Z03SmjXh4SPthm9+yb7o3u01vdo3z+8vlY1mQVd5VZRo/lcnfrtGguv3hb19cXPEiy/xF7teR5wjW/q/z5vVXp8a6nD/q0Bg3Yfe+8RTmcnC1OmjjKtr+WlZZqr5ay+vrpSmbSt3buY3/pVVt99C+3Ne1y51+JTR7SttA5kbWv58Lb1mxs93ljv5tqWxKhVYdzz8GydWL8KVw5vWzdvNXgl6U2zXOHcdvnnNs+upT9BoRiWmD1U0FipWlxJZg0tFlTUzgPC9Tu4779L6+377L4lQikPbI/HyW8GpWoibVTXCyrL16ssfWJBAODVT6O99BrB4nM8cGXeeTziW9uP+fL7LdYfdunu2Qx2HuIm6jNVP1qxillZoLh0nfpKk4UrZT5zo8733Kzz6nKZaxUNq/OA6P4v4/zqt1j/xnvsvbND606bB7bHTvKiUL58V0ztyPJtujJvtBy+8bjHl3+lxebmm7S3BkeWr377M9RXmtSXi3zX7Sbfc7POJ5dK3KzqonyP3sF559+w+/fvsP/tPd6+0+afH1K+uqZwq6jx4nOLNF+os/S556i++mn0Fz6Lv/wSm67M3ZbDrz3u8Wt399neGtD+0oDBzr/D7fyTA+UzSnVKK7eorzS5/ZnrR5TvG+y+/i/Z/1d7tO60WR/5/DvX558n5csr3VtFjeXrVa5971W+6zO355bvG497/PrDDn//YZf21gB7b4PRLz7G+8ditkpemafla6yU+P7vvcHnrtX47GqZm1Uds/+Y+IM3cN75Bq1v3Wfnm9+k9W9E+R67Yrry+1HMg5nyLa6UWPr0Ioufuc3aD/42jFe+G3/5JXY8hXf3Rry+0eXrDzs8Wu9Nytd6LO7fWx7S2yMU/duY1RallftUFuosXq3wuVsNvnCrwR//LTdy5TMYvdWn9fYWu29tZfcvLV96/yxFxtMVWkUNeaXEKClf5RMvU/2B30fhD7+E4Sl090bsbXT5ZlK+zs6Q4c76pHyBJ9qtbGFuLVCKb1HZrrN4z+Nzt7az+/ejn17iP78REK/dY3z3LXadO+w+2qL9QYdHA29u+Rb06fvXeOU69U+9eDG8+Rj/n8FHO0xUXIyLn/8xzPoypeVbLKxVeOFWndduNfjutRovL1hUvTbSB19n9OZX2H/zHjtvPGbn2y0+sP0MZJD2BhSuWSrXk2lvy689R+O7XkV/+bvxVl7h4TDkWztDfvNBh6/c22d/c0B3a4fhzgdZ+AeESrHqyxSXrtO40uTazTrffavBF67X+eRigUXVQ914k/Fbv8H+G++x/Y2H7L29P7dyVzWFa5bGzZpB8+UmK5+7wcJrn8R45TXCtU+x6Ru813L4zUcdfu3uPrubfVqb+9i7idJzhwCZyis016ivXWH5epXP3KjzvbcbfGalzNWijL79Lt57X6f9+jfY/cZ99u+0ebg1YD2Jb+eBumyoPF/SaT5XZ+Xzayx9/mXMV74Lbn6WrrHA3bbDl9e7vP5B+/9n772Dq6zWv+9Pes9OLzttp/dCCCEhEAi9gyggHEVBlKJoBEURG0dseKwIiqKIgoogvZc0SC+k9152stN7T9b7x4Z9Ds953vfnjGfO855nzpq5Z5LZ99zruta6ru/3e62197opq+qgteGe+rwHBmJ8DHVN7Xubq1IkUgcs7Y3xdjIlwtWcQBsjXE110GuvZLwqj+676TRnlNJc2Iq8pouqvhHV/gEobboPAtaOEmyCrDH3dcZ4QggaHiEMmDhS0TFERkMXadUdFFS201zXRW9zA30tdQz1tKsUsfKbKxZI7FwxtTbE3dmUaR6W+FkZ4WWhh8mAAmrz6M9NozmjmOY8Bc3l7ZT3jtA6/GBMWdzbY3K0NrhnkxNmIRPQ8p7MiKUb1T1jZMm7yajtJKuyDUVtF91NTcqq4n8TU0ZSN0ytjbF3MiHC3YIJUgm+lvpYqfWi0VjMUF4yivRCWvIbVDFVNzBC18iYaunQTFsDqa4ypqz8rbD0s8cixBfdwKmMWrrRMKJDXnMf2Q1dJJS00NbYQ1tDK71NVQx2tf5TTBlaOyOxtsDaUUK4uwUT7CUE2hjhoDuGVks5Q/lJdOQW0Xy3EkVeC/Wt/f8UUxItDSy0NXAz1MbS2xwrf1usQ33Q9QtDSL1o1TKnvH2Q9IZOEkpaaGjoprWhm57G8geqr/sxZWDpgLGNVBVToTJTnp4s+9PLNvae/uKFb878oXt3znD/j1om+o8nA8NJGzC0liGR2mHjZEKktxVhTqYEWBtiNdICVdl0pyciTyxAkdtCpbyHuoER2oeVyaGnoaYqr6VOEuwm2WE7NQBdvzDGnEOoHdTiblMPsaUtZJe20trQTWd9Of1tckb6uoC/J6qJoxdmtkb4ulswy8uKYFtjXCUaaNdnM5iXjCI5B3lKNc3l7ZT0KFX1wJh4IEFdrPSx8rFAOsUTk9AwNDxC6DB0IK+5j8yGLm4WKGio7KCzUUFPYwWDXS2qJNCVWGBg6YiZgx3WjhKivK2IdDbH01wXs345Y0XJdGVlIk8upim7maq2AdVYgHIs7oO81Mscm2B7bKYEoRs8gxEbbyp6BCn1nSSWt5Ff2oqitp1eRRX9rXIVQGgZSJTgbueKjZMpPs6mzPS0JNTOGDudMTRrMunPuo0irRB5ej01FR3U9iv3C+4Dg5KUtXCRGiGdaINViAfGk6aA+2TaNE3JVfQRV95KWkUb8qoOOutrHwB0dU1tJRlbOmLhZIurmzkR7hZMczLDw1wXg85qRvNu05qcjiKrmqZsBSWdgw+IA0NNdaS69+LC3wrpZBcsQvzQDpzOgLU3VV3DJNV2ElOkoKKqg5a6NrrrSxjsalUB+H1QkkidsHKUEO5lRbizGcG2RtiqdaNek0tvegKNyfk0ZSuoqe2mun9EBZL3BYqrgRZ2UiOkoVKkEQHo+YUg3EJpGDMgr7mPuLJW0opbaJV301lfTV9LnQocNbT10DO1xtjeE3NbCZ7u5sz0siLEToKHmS66jfkMFabSkphBQ2olLUVtqti8T7T3q1EPM10sfSyQTvHALDQELe9QekxdKWwdIP1ebNZWdtDe0ExPYzmDXa2qCuD+WJg5OGHpYMx0H2umuZjjbaGP1UgLoiyNnrvpNCYX0pjVRK2iTzUW9/PUVEtJFg5uptgE2WI7NQC9iVGMSn2pHdQivaGLpMp2MktaaK7tpLux8oE8Hck+8qfB2dHLX+z47tz/fCMQPdX1v2Tw72rqBpbCaPLTGNt5YGZnhZuHOQv8bAi1N8HDRBOt6nQG7ibQEJ9NQ0o9tQ09VPQNqwLs/nfl3SQ6SCfaYDPZHfOp01DzmkKzpjmZjT1cL2omu7yNxqp2OmuLVWr7foAb2bpi6WyPg8yUef42hNub4m2hi25DNoN3E5DHZ1CfUkdddScVfSO0DI2qgttBT0sJvBOssZvmhdmkENT9IunQs+FuUx+3SltILW1BXtlBR20JAx0KRvq6UFPXQNtAgqG1DAtnZ6wdJczzt2GmiwUe5jroK4oYzrtDU0I69YmVyCs6VAl+v+/7oO8QYIX9VA/MJwWhGTiDbmMnshV9xJS1kljSQmNVB+21FfS11KmSSstAgpG1DHMnF6wcJcz2tyHK1Rwvcz0k7WWMFCTRfDuN+qQK5MV/B5f7AGetowRZmY8F9lNcsZwcgPaEmXSZuVPcNkBsRRsJJS3Ul7fTVlOpAnpQEq+hjQwzR1esHSVM87Fmjocl3hZ6mPXWMpoTR1t6NvV3SlHkt1DYPfQA6ar6djfDPsIF68n+6ITMptfKm/L2IWIqW7mW14SitovWqqoHSEZT1xB9Cymmjp5YOUiI8LVmloclflYGWA01MZ6fQHt6Bg23i1HktVDYMYh8cETVt6WOJg56mri6mGIf5oBNuB/6k+cyaBdERecQ8dXtXM5pRF7bSUtVPb2KahXZa2grD6EzcfTC0l5CiLcVc72tCLQ2RDreznjBbbrvZlAfX0BDWiMVvcqKoHd0/AGCdXMwxi7MHpvJPhiGz2ZENonqnjHu1HRwJb+J6qoOFJUN9DRWMNTTzvjoMOqa2uibSzG298TSwZQJ3lbM9rIi2NYIR80+KE6kJzOF+oR85GlyKjoG71UjSjJRCh1NPKWG2IfZYRPqg1HYDMbcwqgd0CCprpNrBU2UVXWgqFKoSPV+3/fJzMLODB9PCxb42hAsNcZZbxSNsmT6spNpiM+hLqWBmpZ+FYnA34nMTaLD4vq8P08G3v5i5/fn/9C926a4/JcM/l1N3cBSGIdvwkTmj6WDKZP9bJjvY80EG0OsB+oZybhGS/JdauJKqShtp7r/72As0VJHpq+Nl7ME6SQ7HGZNQndiFH3SQLIa+4ivbONWbiMNFe101pXS11zH2PCAKjhNZP5YO5owxd+GhT7WBFjpY95dyUjeHRpjkqiJK6euupPSe99SAlQqz9neGMdIGdKoUHSCZ9Jl4UVWk1LtxuU20VTTQUd1Ef1tclWf95PRzs2SSH8bZrhZECI1xKStlOHsWOSxqdQmVFFZ00VF39+TwUxbA5m+Fu4yCU4zXLGZEoRO6DzaTd3Ja+7jalEzt/OaaK7tpKO6gIEOBWPDAypFaSrzRepqRriXFfO9rZhoa4hhaylDaddoiEmjPqWOsuouKu6dGPqPfrq7m2EX5og0ajJaIXPpkjiTIe/lcqGCjJIWGspb6KotZKBDwfjosArwTGXe2DiZMiPAhnmeVvha6iFpLWYw5TKNibnUJlRTUtutqmrug52HoTYuXhZIw2TYzpyClv9U2oxdSGnoIa6slaS8Jhorm+muL3mgTwMrB8xlXtjITJgVYMsCTyu8LHQxkOcwmBlL3a10am/XUN7YR3W/0s/74O5xT6k6zfDAKmoqmn5TUejZk6vo5VKBgvQCBYradrpqC+lvk6tIxcDKARN7N+zdzZkXaEuksxlB1gbo1KQzkBlL3c0MGtIaKa5XVgv3Qf0+mbn6WuI4wxOL8EloBM2iUduaTHkPVwoVZBUoaK5pfgBU7/dpLvPA3t2cKG8r5ntY4WuuhXZ9Nr2J16i9mUlDeiPFTX0PEImljibeRto4+lliP8UNqxlTUQ+IQqFpQVJ9N5fzmygoa6WpXE53Q6mKwLQMJBhYOmAuc0PqbMqiCVJmu1rgbqaDdmUK3fFXaUwuoDaxnsLmPtX+3f3lNR9jHRwnWGMX7o7F9EjUvacg17QksbaTWyUtZBcoVMT1j30aWcuwcHbFzsWUJROkTHU0xcNMFwN9vT8Nzk7eAeLVI3+MDLaGO/+XDP5dTd3ASphOfwELV1/s3c15JNSBOa7mOGr0MJ5xBfmVW9QnVVNQ0k7FvRdeaKurIdPXwttcD+fZzjgumIpOyByajN1Ia+jmdHYD2bkKWquq6KovZXSwF3VNbQwsHbB098Pe3ZyVkxyY42qGPV2MZ1ym4UoMdYk1FJV3qMBCT0MNqa4W/tYGOM9yxj4qGJ1pD9Gk70RqQze/320gN1dBa1UF3fIKVT+GNjJMHT1x8bViZYg902SmuKh1MpZ+iab4FKquF1NQ2akCiPv9BEgNcZnlgt3MULSnLKHV0JG46k5OZtZTXtqGoqxM1c8/AqCrrxWPhNgzzdEUmWhlPC8O+dUYqq6XUFLb/U/j5m9vhEOEA44LpqIdvoQWfXtiqzr4LbOeitI2FGUl9DZVq/rRN5di6e6Hu68VCwNsmedmjsNoM2O5cciv3KLyehll9yq2+/446GnhY6UcN8eF09CeOJsmQxdiqjq4kt9Ibq4CRVkRfc11D/hj6eaDp68ViwNsmeVihr3oYCz9Eg3X4qlNqKKgspOKvmEGxgR6GmpKMWBjgMssF+wXRKIVPAu5vhOxVR1czmskP1dBW03lP82PpZsPju7mrA51YJazGdKxVkbTLtFwNZ7ahGoKa7oemB8HPeW4Oc9yRTo9BO2IZTTo2JFc38XJzHoK8xS0VZfTI69QEbGhjQxzmQcu3pasnuRAhKMJTmPNjKZdojE2hcrrZRTUdatA+x/7cZnrriTgyYtR6EqJqerg96x6yotbaa4ofaAfI6krZo6uuPtasSrEgamOEhxGmhhJvYg8NpWqmxUU13f/07j5Ohgr42DxDDRDF6HQtuZWVQenMuupLG6luaKY3qZqVT//6/zMcTXHbqiBkaybNFyNp+pWBSXy3n/qx18mwSHCEftFUWhNmEmDrgO3qtq5lNtIcX4zirIClVC7T3hWrl74BViz0N+WKGdTbEdaGMuJQW/exn8JGbz2w4U/dO/msD+/R/HvbP/xZGA++2VsPH3w8bHk0RAHomQSjBX5dFw5Sfm5DCqyFeTf+8n9fUALcJQgi3LGfuk81EOXUDagy+/5jdzIllNTUE9ndT6DXS2oqWtgaC3DzNkHr0Ab1kxyINLJBNvucnpvnaL6WiaVCbVkdSp/PXlfuU200Md1nguOcyajHfkIVRo2xFS182tyDdWFzXRW59HfJgdAz9Qac7cJyHyseGiSPQvdLXAYb2M06QzV52KpSagjs7EX+eAIYwJsdDUJNtHFYbIUtxVT0I96mGYjF25WdnAspYaaklaaS3Lob5MzPjqsPJvHwRtHX0eWTHZgoacVXrp9jKecQ37jNhVXS8iuU+6jDI8LJFrq+Bnr4B0qxXGmH+aLH6HTbiLJ9T2cyKwjK6uRlqoyFaBoGUgwcfDG3tuZiEBbVgVJCbLSQz37MopLl6m+VUx+URulvUMMjAkMNdXxNtLGw9Mc10UBWC1axqjnNJLk/fya1UBmXhPy4nK6GysY6etSLQvZenoxOVjKUn9bpjsZo1cST0fcNcovZFGYraC09+8kLNPXJsDVBKcZrtgtmQ8hiynp1eC3HDk30uuRlzXQWZPPUE+7CuBtPP1w87Jgbagjs51NMGkppOvaKWpu3qXkdh359w6b01ADmb42fhZ6uC1wx2HZXDQmzKECc84WKriS1UB1gVxVCaipa6BvLsXcLRBXP2tWTLRnvps59v1VDKVcofJsPJUxNeR1DSEfHAGUMRRsqofDVHvcHopAJ/JhmvQcuF7ZzvGkGmpLWmkty1ZVGnqmNpi5BWPvbs5DYY4s8bRCptbBaPJZGq7foeJ6OXcbepAPKpfqLHU08DbSwTPMDrflkzGavphWS39uVnZwLkdObk4TzSV59LXUMT46jJaBBFMnPxx8ZcwItOWRACn+RsOQF4P84hXKLxY+QBgSLXW8jXTwDrZBNscPi4XLGXKN4HZtN79k1pNToKCxuJSu+lIViJs4emPtJiMi2I61E+2YYGOAZt51FBcvUHOrmIKCFlX1aaipjkxfCz93M9yXBGA5MwoRvIg0xTAXCpqIyahHXlxJV0MpI31dKjHXfuX1Pw3OMu8A8frRP0YG/4oN639n+88mA0NrYTXvNez9vIgIkrIuxAF/g37GM65Qc+oSZRfLyOlUrl9qqyuV0wQnCZ4PBWA9bw7jIcuIrenmVLaclLR6miuK6ZFXMD46jJ6pDRYeE/EMsuWREHsWu5tj3phFx/WzlP6eTk5Os0rNmmlrKAE0wh6XJaEYzl1Nla6MGxVt/Hy7iurcGrrqihjsakFT1xBjqSt2Pp7MDXNkhb8tgUZDjN3+jforCZReLCX73sautroaUl1NQmQmeCz1w2bhvAdsTs1ooLEwR5W0StCZgFuADatDHVjobo6VIpuO62cpOZn6TzZ7G2njN9UB95VT0Yt8iBo9GdfK/9nm+wrSwc/nQZsTf6f+UiylF0tJv3cUhIYaOOhpMdHRGM/lAVjPm40IXU58XR+/58hJSqv/J5tNXYLwCLJlfYSMGTITlc2lp9PIu6sE+n+02SfcHs9Hp6E3dRk1Bi6cLWrmXFodVXl1tJVnMdLXpbLZzsebOWGOrAiwJVgyytidkyqbM1v7kd+z2dVAG397I9yX+mG34iHG/Of8jza7BtiwIUJGlLMp1q15tJ79hYrL2f+vNnusjEB/+kPUGrhw5h9s7qjMYainHQ1tPUwcvbHxcGVOmCPL/W0IMRljPOl36i/HUnaxhHRFH4qhUeCfbR73m8Vt+SBHU2vIzlXQWJRHb1P1/2hz1bVccjIaKepREraZtgaBEh3cJtspbY5cTr2RG2eLmzmTVkdlTu3/1uaoUAfWhdjjYzjKeOJJqk9fo+pm1QM2y/S1CZAa4rHUF7uHlyP8Z5PYNMyRlL/b3COvQIyPoW8uxcIjGJmPFU9MkTHT2RTb9gLazv9C5eXsB2yWaKkTbKKLa4it0ua5a2jQdVDZXFPUQvW3K/88GfgEiDd/vPiH7n1qktN/yeDf1TQMrYXtsvdwDXZl7VRnVnhbYloWS+OZ0xT+dpfkum6aBkfR01AjUKKL32QpPk/MRnPOExQO6HMwsZqExBqaS3LoVVSjrqmNiaM3sgm+LAl35LEgKQ4d+bRdOEHB8RRSilqpG1CqN28jHQJ9LPBbNw2jpU9QpWXP0cx6rqbUUpOdS3d9KQCG1jIcgiYwfZIDz4Q54TlWx0DMSfJ/iCUzs4nS3mGGxwWuBtpMcJLgsyYE6+WP0Gwfxm8FCk7eqaEyq5iO6nzGhgfQN5di6x9KyEQpW6Y6E2I4wFjSaUqOXiI7rpb87iF6R8ex1NEgzNIAn9WB2C1bxEDwMs6XtHE0qZrSjCpaS9IZHexFV2KJpVcobgE2PDvDlZkyCVppp6k6dpLyyxUk3Tuh836yBSxyQ/bwPNRnPMaNukF+Sq0hM62e5sJUFdmZyvxwCXZn40w3FriZYVZ5m7rjxym/VEhydRfye3PiZ6xDcKQj7iunojP3SdL7DPgpo564xBqaS/LoaaxAXVMbY3sPnCf4szrKheXeVsi6ilD8dpSys3dJLWilom8YDTXlnEycaIP78hBMVmygVteRo5kNnE+ooqGwiK7aItWc2AdMYOE0GWuD7fAWTfScO0Lp6VTS0+SqOZHpK0nN59EQbNeso9k6iFOFzRy7VUFdQSXtlTmqObHxnURkhBN/mWhPuOkwY0lnKP3xIndvVavmxEZXkxBTPbxX+uL42FqGfOdwvqSNb+IqqMproLVUeeS2jpEZFl5huN6bkwUuxqhnXqDqp98oPF+iqkTvg7b/fFdc162AyQ8R0zDEwYRKirIbH5gTc7dgnPyc2DDLleWeFhhX3qH+5+MUncgmq7mfuoERVdUWFOGA95Pz0Jr2CNmDEo5l1nPzdjXy/Az6WupQ19TGVOaHvZ8Hq2a4sMbfBtuuUlpPHqHgWCrZVZ335kQNVwNtJk2wxn35RMxWPEmZrjO/5TZyLqGKmuxsepuqEeNjGEldsfX2Z8E0GRtCHXAbV9B3+Sj5R+LJzW+hqGdINSehrqZ4PBSI7Zp1NFpN4EJpKz/GVFB9t4jO2iLGhgcwsHTAynMC4eGOrAt1IMJWF11D4z8Nzs4+AeKvxy7/oXvXTXT4Lxn8u5qGobWwX/UpAWHOPD3VmXn22oxdOUTJL7GkxNWS3z0IgIehDuFT7PBaPRXdJc+Q0KHD98nVJN+pobkwkZG+LvRMbbD0CmHqVBnbZ7jirdFO37lvyf0ujozCVkp7h1UAFjLbGc+Nj6AWsYpzFT0cuFFKZXY1raXpqkB0DJ7MrHBHtk5xwqWrAMXJn8j/MYXE6i6aBkeRaKkTYa6P5xIPXJ5aR4fXHM6XtHL4ehk1OUrwF+NjGNt7IAsOZMM8D5Z7WWJZEUf1kaMUny5SAbWljgYRdsb4rwvFZvVj1FoEcSKviRM3y6nJTFclsLlbMM5BLmyZ7c4KL3PUkk9S9t0JMq5WqgDLQU+LKZ5mBDwVicmSxyjWdubz25UkptTRkJtKf5scTV1DrHwjmDhFxsYpMmbZqDF86WuKj8eTklhPae8wY0LgbaRDcJA1Ac/MRnvhM2R0afFxTDmZSdW0laYz1NOOnqkNVt6TiJruzLZpzniptdJ96muKf00hKVtBRd8w2upqBJvoEjLfBdc1C1Gb+SRnyzr5Nr6SktRS2sqzGBsewNBahtPEScyf4sTTkx1w6q1AcewQeT+mklrXjfzeuE8y1SNwhTfOG9fT7RHFiYJmfrheRk1OIR3V+aipa2AkdcV10gQenibj8SBbzItvUH/yd/KO3yWxrZ+uESW4R8gkeC4PwP6pTVRKfDmR28jJW+XU5ykFgYa2HqYyP9xDvXl2jgcL3UzRvPMzJYdPURBbQ3rHAANjAgc9LSJ8LAh8ajpGSx6nUN2e71JruZ5QhTwnhYGOJrQMJFj5ROAdLGX7LHcizUcYjf2Z/G8vk5bcoBp3P2NdgoKtCdw0H62ZfyGpW59PY8vJy2iguTCZoZ52dCWW2PqHM3O6MxsnOxGooaDjxNcU/5ZGYm4z1f0j6Gkox91/uiNeTy1HzFzP1fIOvrhZRmmqMkZHB3sxsnVF6hfEilmubAixx6GrGPnRQ+QdTSNdodwYNtRUJ8Jcj4DV/jisWkFPwGKO5Tbxa3wVFek5KqKWOHrjOimQR6c7s9bfBkneRep+O03uiTzSOwZpHx7DRleTydYG+D8RinTd09Sb+vBdej2nb1Ugz8+mp7FCJUo8JnuxIdKFRe5mmBsb/GlwdvEJEHuP/zEy+Evwf8ng39Y0jGyEy7qvWDLXnU3hjrg1p1P+xQHyzpWQ2NbP8LgyMaYucMHz+afo8VvA12n1/HypRAVs+uZS7AIms3aRJ49PkGJTco2SL74j83oV6R0DaKsrq4rwh71xe/5ZGqWT+eR2NReul6EoSGGwqwVDaxmOwSFsXOLNal9rjNNciCB6AAAgAElEQVROkP/5z6TdriOnaxBDTXUmmeoSum4iTltfoMLQkw9iyomJqVCRkcTRG5eJQTy32JuHvMzRuP41dz89Q0qWsnow09Zgirk+EzZNwXpjNIVqUvZcLSYttojW0nTGR4cxcwnENcSXHYu8WCjTZ/j0p2R+cY2Uolaq+0ew0dUk3MGY4C3TMXnsRVJ6DNhzuYjC23m0V+YAYCrzw2daILsXejHVZIieXz8n60CMisTuk8XE5+eh83A0NxpG+PBiISVJd+mqLVKRTkiUL2/M8yJQQ0Hz959x91Aiic19tA+P4WqgzZQgaya8sAS1Jc9zpqiVLy4VU5aUSk9jBVoGEiy9wpgx051XZ7nh3l9O3defk/FDBintA/SOjuNnrENouB0B0Y/SH/EYJwtb+Pp8IdXpKfS11KFjZIa13xQWzXFne6Qz9ooMKvd/ScrJArI6BxkeF0qCme2M9wvrafFZxE/Zco5dKqE24zaDXS2qKmzVfA+eDXPAtPQWJZ8dIulyBfndQwBEmOsRuNQDt21bqZaG811aHaevltKYl6yKDYegEDYs8WLDBFu0U05S+MUxEmJqKO0dQk9DnUgLffweDUD27PMUGHiz/3YVN26WPxAbsuBAtizy4i/epozfOkL2Z78Tnyqnun8EiZY6UQ4S/B4LwXbj86SOSfk8roLkuBJaS5QCxVTmh+skf7Yv8mKJvTrDl78h64urxOU2Ix8cxVJHgygPc/zWhWP6eDQ3O/T5LKac/NsFtJVnAWDhMQn3Se7sXuTNdKNues98S+aXt4ivaKdlaAypriZRQdYEbIxC75FoTlcP8dXNMoruZKliw8o3goAwZ96c70WwqKX1xGGyDiYQJ++ha2QcVwNtIsPt8N84F/Wl0RzLa+a7m+WUJafTXV+Kpq4h0gkzCJ/iyK7ZHnh151F/5DCZR9JIaO1nYEzgZ6zD1NkyvJ5azmDkE5wqbOG7m+Wk7J7958nAN1C89/MfI4M1Qfb/JYN/V9MwthVeG7/hqYd8eDLIFr3Yw2S8f0KlbBz0tIgMtCLklUcYmLWJn3Kb+PZMITVpcQz1tGNs74HvjDCen+vBUuthun87QNrfrnOroYfe0XECJbpEPeKF66YNVDtF8dbVYpISKmnKiQWUyRExx589C7zw6CmkbN8+Ms8Uk9jWj56GOmFmeoRtnoL1lldJ6TfhjXP5FMSl0VVbhJaBBMdJUSyf7cb2aTIME45QcPA0N+Nrqe4fwVJHgzmB1gRtno326lc4nNPMkcslVCTfob9NjoGlA24REbz8kB/LnPXpP/YBqX+7RkJ9N+3DY3gYahM1S4bvK5to857PB7GVnL9SgiIvgbHhASw8JuEV7s0Hy/2YOFJG/eGvSP4ulZT2AcaEEuRC1gQgi95JqpoT718vJfNWNm3lWahraiOdMJOHF3mxJdwRu5Ir5H3wLckJymUqiZY6M2UmhL2yEL2HtnK6TvC30/lUpWfQ01iBvrkUp5ApbH3Ilyd9TRi7coi0D89wK7+ZliElWURMtCF412N0h/+F77Pk/HC2kLrMOEb6ujCR+eE7PYSd8z2ZY9xJ48F95Hyfxq3mXsYEBEp0iFzth/PmZyi1DuONy0WkJ5TSXJCImroGlt5hzJzry5tz3XFqzqTkg4+JOVdKUY8SnGc7SgjcOAWLja8Q067LO2cLKL6TSnd9KTpGZsjColg5x40XpjiidfUAOQcuEJMip25gBKmuJrMm2hL07DzUlr/EgbR6jl8rpTIpXkUOnpER7Fruy3xb6PrhQ9I+uUWCopeukXGCTXQJn++C987nkTtNZe/Ncq5dLaSlKIWx4QGsfCLwj/Dgg6W++HbnUnPoKxJ/uktK+wAaampEWeoz+blIrJ/YSvyQDR9eLyXnVgbtlTlo6hpiFxzF2qXePBPqgEXmb2R/+COJKcqqwkZXk0h3M0JfWYzGwzv5Jb+Z/WcKVAR7v/KKXuHHGjc9hs59SeqHl4gpb1fF3KwFrnhtXkNL0Ao+iK3kWkwFDVmxjA72Yu4WTEDUBF6Z68F0nSZqP99H9rEsYlv6AZhkqkvE4xNwin6FPE0nXrtQyN1bWbRX5qChrYeVbwQL5nuxa6Yr0qp4iv52gJhLFRT1KGNujrs5QZsiMV73Cicqhzl4tYSShER6FdVoGUjoT/zsT4Ozq2+geP+XK3/o3tWBdv9RZPAffTaRGqCtp4m9RA/DsV7aCkpQVHaiGFKuSQdb6eP5cAjqM5/g53wFRy4VU5MWx3BfF2YugYQtmMxHS31xbkwmf8vHxN+opqhnCAc9LVZMsWfiWxtpCHiI5+MqufjF7zQXJqKhrYdsygK2rPLnqQm2jJ94n6QpmzhS10XvqDKZX9o+DYftr5PQb8bK33IpXvcjfS11GNm64jcrglcXrWOuXhOVH7xD4roC3mgfwFJHg+muZjz//QbUlr3IJ4m1bPg9H/k3cYx9GY3thNnMmOnOuRdfxSLlJ3I+/Y1rHxwn5u1R6g21mbvUnblXvsTByJ/tZ/I4H5/Lq6Xp6O3OxDFElxdX+fPRe2F0/5xM0vsx3My+Cb9B4/sGtO+ah82Od+h7eJzbF4ooux3P1x1NSKq9CbzUz+cPa3FuxjiF8Re5XlpGae8w0txrzKtwwFZjFS3h69i/xo046zIa795EXVOb3yfOYp2jL89qmbC09EssTp1UKcgwMz0i3ItxsXmD6/W6fFAbSr7VCD3eFRhYOuA1IxKXJd5oWvTR985mnH/MZFZrP4aaSgU8Zak96qsn8d3dRl78qpjaNAPGbMOwmhXBgiVBTI9yxaHgPHlv/Y1rN6qw7B9hq74W8+Y44//WdqrtI3jlQiERW47SXpmDtoEnrs9uYeMSb572ldD1zR4yv4zj+huXGR4XbLPQJ/LFKCyf3MXpFgPeP5HDJx/+xN42OSYyP3yW/JWPfggguDeH8s++4MaJAn5aexhLnSPMD5Hy7AtL6Hv1LfbequDSjTLunjnJ6nOnsfaPZNWyjbxW9C4z446Q+vYxruc1k/VrIcFXo5m6xJ2v39xNYsg89lx2JOdGMor8BLobSnlK0csbKwNY8NoHRBm/g8bBRFLaB0hsG0D/x1R0TAyJWP8mq0MdaKjsoEdRzUhfF32tTZQp7OkaGsPKyBQNrb+fzW+goY6h1BBtF18qu4ZJKG2luVJ5npCWgQRbnwk8MtOV1T4W9P2wh8wvb3G1tI2BsXEizPWYsXkKNs+/wc12Pd7+MpmShHj62+QY23vgGRHKO4/4M0O9hur9L/DD0SzSOwaRaKnzUIAVYXv+wujsp/kwvprj7xbQlPMNYnwMu4lzWL56GnvmuCHJ/J3MPdv49rEGWobG8DbSYd4qb57dtpV8i1BePJlLUfxd2o+8ir65FFnoFL58fwOPuOgycOozTBL/Nbij8X/n0UT/2WSAmjqGJrp4WxqgXpVMbUwhWZ2DDIwJZlsZEP7aInQf283bsTUc+zWN5sJEdCWWhKxcw56H/YkaKSBz3TJ+S6ijbmCEYBNdXn9zDpYvfcS3+R2s++kudS9vR0NbD4+o+Xyzey9zDFspfe1Vzs8t5qX+EQIluizaEs6sF17n+xpNPj2Zx3O3r6CWeACHkJm88kQw6560p/7z97j49Y/kX/mWnDe1cF7ji9ue9ynaZkryj1mUxV9l3/AAjglePG5WwyuTLVkXe4nb5ckktPbjUBTHPLkjtsFvc91lBX+N8qBg5DpDPe1Y+UTQvnYyb0il2PzwDhv+eomE1n7MtDVYMsOJoCUBFNoaE3U4i6I4Cb02oVjNjGD2oglELPFC45e93ApZRHZdNzPU1fjIz5IpHz9LU+AKtpzMJfKp/fQ0VmAim8XsA2+zd6EXtklHSHjpB15f9hFjYh/rfCw5uPsh2vd9ylvXy7j8eyJ7d17kU0sHghbO4pvYLbxRcZ3U7Z9wMbOR9EMZLDj3GDM+Wsm0LS/zspMp504m0VqaTlFMLPtGx7FdF8yk1SvpLFdQdK2SlqFRWroGGWjpwFhdjYHhMQb7BhkbHkDbQIKNm5S/TLTHpaeEvIPHuHmzmur+EcLM9Ji+PgS71z7ki4IBvtkbQ3XSFTR09HCbvpQ3nghmpUkrVR+9woGj2ZT2DuNtpMNzz05Gtn0XJzstmH00i+qVBxkbGsB56kLefGs9z3nrUP/uTmLf+Ybvtg8SZ6DN3IWubMn+hfgxB3b+lMVnsTcY/qAM22sX2PyXYPI/XkDX/liu77tJSvol9I9rUjXPBf83XkD71EVu/ZhFSUI837fJsRiaxIocfT5YoM8Z45vEp//OlaYeDDXVWTp8m0lzo6m0mMNm08fJniClr6UOc7dguldOxW+OO7rnP8Vx7znmlLSioabGHGcTIv8SREO4F7svFRF7rpLORieMps1k8tIodq0MwObuSRK37uN83ksYC/hOZkL4a4sYXfUa0ecK+er72+x5KRFDaxk+6/Zx8PFgvCqucPevh/jqozi63rtFlKUBV99egtrZN/kwvppjv6aRcfIXll+9hu/cuby9+TOe3FRC4Pa3uRZXy4lsBY1PfU3k1jze2PEh2prq/KipTl3qFZoLk0k20yPN14b5npMwdTmFQUYjLSi/Kq5rLgErZzLqupBXtNMtr0BDWw+H4HCeW+7LKpsB5B+8Qfy3/4LXnKF805mWxp9/uc3/H9t/OBmooW+gjYWeJsOV+bQUtKo2mDxnOmG46HFOlXVy+mopzYWJaBtI8Jk9i2+fDMGt4irxW/7G2YIWtNXVWBfpSMh7z5FqO4slHyRTfvsWo0MDeM15iF1rAlllUEf6lkfZe6eO9uExlniY8fzeNTRM28RfjmaQv+5H+tvkWPtH8td9LxLtqUbdh2/w+4ydPNc3TJiZHo++sxjx5DvsvlpK5C8xdC7+CDOXQB5ZG0XaG+/S9dXrXHhvF+kHBzlgqM2KreGsLLxKVe4gX/+QxJ78BAyjY4lYocnxZ0Kxdk7m+osXuXn3BtqnNKlY6Y3X228xPOUF4n/Opjz+Ip8PSvC4acpXTwliQutJO3eN03lytAviWFBijbHrK9ye+SJvN0WRf/0yY0MDXJq6kPesJrG0v4r3buzh95hy2ofHWNiawcz1mrSoe7O4egKZjs30G8iRTpyH61OhTPXSY/CNDUz4KQet7iEiLfSZ8bAOxs9M4rmzRVw/10zroCfmy9Ywe1kY01f40vP5S8Q6TUS09vOasQ7z1vrj9N57fFM8wNq9N5FnXkPfPJSJH77BkccmYJl0lLjnvuHiq16YaWtwZr4rAac/4nyPFa99l868R19HQ0cPv7nbeffdIKI7k0nc8j5ff5HI8GcRrJgk5dl3N5G74322fJdGaXwMT96+xN7pi3l5/ce8sKOF7Odf4dLNKj7Zn8LsE2tZ8ukaJr/2Ai+etSTh1HUq4s7ySXMLAxumsfO1D5ij+xa9B5Io6hki9UYVNiFHiNi6l4ggKfX5Dgz1KI/eLlN40D08jqa+LmMCxoRSjUucLRB2XqSUddJU3cJAhwIDSwdcgr3YNk2G+pUDJL5/geuKXsy0NVg8zZFJB/ZyatCZdz+5Q2nsJTR19PBduJIvngplcuMtMpY/z6dJ9QyPC5b7WjL1wHbK3Rcy+4cMcv+2h+G+LmRTFvHF26t5xLKXoh0vcuTFUpoGR1loY8i7v2yhe+6zbPglh8fPJ9L15XPYBEaxatVk3o96iIa3nuPa+z9z8NVBwsz0mLVlCm+ev8zzF0p44UwaLd+kYHbzWyIWhZFzcBVaZ6q59vJJbsafIGOvFk5rfQn84Tuyq9X4/vsMvk+9hG68GROMcznyxER2ijvE38zk/N12JKlnsMmWwbsvU/3KtxwzT6cy8ToA3+ku4u1OY57UycEz4yOuZDUxMDbO0uEMwmc+RZHDarZZPUluiDNce+vPQw6grvZ/Z2nwH00Gamrq2JroYaY1Tmt+GaU9yk09P2Md3FbPIR9b9l/Joi4jBk09Qzyj5nB4/SRkt7/mxvZjXKjvxttIhyVPTcT2/e95+UoZP+/7nq7aImwCo9i9JZKnLVvIfXkDuy6UMSZgWYgtEUf38UO3AxOOZlG19wWM7T2YvmoBx9YGMnDoNU4te5dtXYNEmOux5dBj1M+OZvPPd9l26gJjv7yK+/T5XPp8PX65P3Pn5e85u/JTvtDXZulaPx6rTqI2uZmPDt/k1etZWNZeJXrzTKren0Tpa7/zy2/x9N48QvWXljge/Ssj1zcQ90Mm5fHn+azZgeDLg5x4wowlHkn8/n0c6R2DTMo9h5vNcppWvc7ex11J0bvEcF8XFyPms988jIjaK3xw5gCnchRItDRY61GHh00wnxfp84nuKpq9EzFzCcR041zmBusytPtJpv+Qje/wGItcTYlaP59bpvqEfpRJcaoZWoFLCF62mNfXhyC59hnnnCaiew/sV70xn/Gn1/Ps7/k4Lv+A/rZhHFe9y74tYSweSCftxY/YbxGOnoYaZxa64X/5AD/UafPO14m4HTmCxNGb1R//xKezbJC//zK/fpnEYedlLLAx4u4P0aRve5ftP2WSe+ksS69cIHDJMn66cp23k49w9ZlDHE9pIH3ZXlZtv83Nlz9nh7cVZ3+8TFnsOd7uHkR9cxhrP9zN2Pa/8tutahJa+5F+f4uQ8MU8OsmBvAxfehXV98C9l3F9WzR0tRm7t/dmpq2BsYcLDYPqZJS0qJSqY4Av26e7Ikn/jZsfXiOlfQAHPS0e2TwZm73f8tKVMn7+4eoDsbfBaZSCLSvZc6GMgTHBylApEUf3cXnMDe/9yVTd+Q59cynznn7877EX/CLH78Xe2z88Sc+iHaw5kkHm7guMDMThOWspMcd2E9gQQ/yGdzh7oI1iHU3WPB7ALkUa7yU3s+nwTdrey8Ly9HGiN8/k7Kq/UB69iV9+fZv2H8dI9LMi8uhf8XnuC748kMyR+PPo3DEj9LtMTjwRzL7O3/j9WhLp2TFMitVFbWg5I4+9ybe9c0g5dYnBrhZ+Nl7K/m4DntS5i2vqvr/Hnl0+9k8c4ku9GXwyCZoNlLGnvnEu/u5S3F5+ilU/ZCMfHOUhdzOi5k4lz1CHkK9GKR71R2tyOMHLFrNjfQhj1z4jLyAcr9Z+5utr/fmXBN9r/7cuEyGE+I+9tEwdxfsxpWKsLFkkRk4TW9WcxGdGHqLhnc2iurVHTHzzqtAN3SpM5rwpnj2VIwa72sTdFXNFtIZM7NJ2EWXPrhQdPX1i8aEkYThth9AN3Soi/xYrOnv7Rf3bz4g9uq4iWkMmEiOnieHGcrH5ZLawWLpPaAdvFCFvXxO58k7RcWiX+MzIQ2zCSdzwCRHDKWfEd+k1QvroIaEVtF64bDolbpQ2i6E7J8Rpax+xCSfxo7mX6L/wpbhQ2CS8os8JraD1wm7tYbEvrkyMFiWIOxFTxVY1J7Hf2EPI39sqKlq6RfDrV1S+PH86Vwx1KETWsjkiWkMmXtdxEeXPrxat3X1iwVeJQj8iWuiFbRPTP4kTPX39ou7NjWKPrqvYoeksUmbPEMNNlWLDL1nCfPH7Qidkkwjdc13ky7tE6/6XxGFTT7EJJ3HLf5IYTjsnvk2tFrarDgqtoPXCdcvvIq68RQzGHhMnrZS+HLfwFgNXDolzBY3C8/mzQitovbBe8Zn4OKFcjBbEioSwKWKrmpM4KPEUTR8+J8qau0XgrktCJ2STMJ23R7x4Nk8MtTeKjMWzxTZ1pS+VOx4TA329Yu6BOypfZnwaL/r6B0TNq0+Kt+75kjovSgw314gnjmcKswV7hU7IJjH5nRuiqKlLtHy+XRwyUfoSExQqRrKuiIPJVcLmkf1CK2i9cNt6WiRUtIrBW0dVvvxq6S0Grh0Wp/PkwuO5Mypf9idVipHsayI+NFxsQulL8yfRoljRJQJeUfpitmCvePVigRhqqRPpC2aKnVrO4i1dV1G1c50Y6O8Xs/bfFvoR0UI/IlrM2n9bDPT3i6qd68Rbuq5ip5azSF8wUwy11InHfkpX+RLwyiVRrOgSzZ9Ei4MSpS/xoeFiJPua2J9UKaxXfCa0gtYLj+fOiOTqNjFw7bD41dJbbMJJnLTyEYO3joqEilbhtvW00ApaL2we2S8OJleJkawrIiYoVGzCSRwy8RQtn28XRU1dwu/liypfdl8uFMPNNSJ1XpTYoan0pebVJ0Vf/4CY8Wm80AvbJvQjosXcA3fEQF+vqNzxmHhdx0Xs1HIWGYtni6H2RrHmaJownbdH6IRsEkGvXRZlzd2i6cPnxEGJp9iq5iQSwqaI0YJY8XFCucoXz+fPirSadjFw5ZA4bqH05bS1jxiMPSbiyluE65bfhVbQemG76qD4NrVaDKedE7f8J4lNOInDpp6idf9LIl/eJXx3XBA6IZuE+eL3xYZfssRgb7dA+W7hP4U57r6B4mqx4g9d/1N/wHygBCgHXv3ffL4dKARygVuA0z98NgZk37vO/yvw9P84oP8pMjCXiR8z68TAlUMqoI0JChVjJXfEqxcLhGTWbqETsklM/yRODPZ0iqKnloloDZnYZ+AuGt7ZLAoau1QA5vDEj+K79BoxeON7ccTMSwWIIw3FYvflQmEy502hHxEtIv8WKwZ7u0XG4tlih6az2GfgLjoP7xZFTV0qYLd/7Ii4XNQkBm8dFUfMvMRWtXvPqssXG37JEiZz3hR6YdvEsm+TxUB/v8haNkdsU5eJ9/XdRMehXaJU0f0ASZwraBR9v30ofjRX2pUYOU2M1uaKp0/cFUbTdwq9sG1i8aEkMdRSJ+6umCu2qcvEXj030X7wFZFc3SZ8XjyvIqbTeXLRd+ojsd/YQ/WsobYGsflktupZC75KFENtDSJ75XyxTV0m9ht7iJbPt4u0mnbhsumU0ApaL2RPnxTJ1W2i/8wn4qDEU2xTl4k7EVPFUEudePZUjjCO2iWMpu8Ucw/cEUPtjSIpKlJsU5eJz4w8RN+v74m79R2qxHZ66leRWNUqOg7tegAohhVVYsV3KcI4apcwnLZDPHE8Uwx1toiU2TNUxN/78zviclGTCvA8nz8r4spbROfh3eKQifJZGYtni+GmSrHqSKrQj4gWhtN2iMd+ShcjDcUidV6U2KqmBJKeY3vE1WKFcHjiR9WzbpU1i67v3xD7DNzFVjUnkb5gphjs6RRrjqYJ/YhoIZm1W6w5miZG5KUidmKY6lndR98SN0qbVc/yeO6MyJd3ie6jb4mPDd1FtIZMpM6LEoPdHWLm5wnCcNoOIZm1W7x8Pl8MN5arnnXIxFMMXDoo4spbhOP64yoiy2noFIqPnlc9q+DxxWKos0XM2n9b6IZuFcZRu8SLZ/PEsKJKRWKHTDxF/4UvxS/Z9cLpqV+FVtB64ffyRXG3vkM0fxIt9ui6im3qMpG3dqHo7usXcw/cUT3r2VM5YrQyUySETVERZ/+5z8WJnAaVWPB7+aKobOkWLZ9vF3v13MROLWeR++gC0dPXLxZ8lSj0wrYJswV7xeaT2WK06q646j5BJSiG7pwQp/PkQvroIaEdvFH47rggKlq6Rc2rT4q9em5ih6azyF45X/T1D4iID2KEXtg2YTpvj9h7s0SMVmeL617BKqE1lPCLOJhcJezWHhZaQeuFz4vnRWJV67+GDPwCxY3S5j90/X/1B2gAFYALoA3kAD7/yz1RgP69v7cAJ/7hs95/NZ7+Hwf0P3NpW7iIW2XNouXz7WKfgbvYqeUs6t9+RlS2dKtA3uO5M6KypVuUbn5Y7NRSgnffbx+KW2XNQvroIZUyHujvF3lrF4qtasqAGrhySOxPqhSm8/YI3dCtYtm3yWKkLl+VWNe9gsVYyR3x9Im7Qj8iWhhH7RJ7rheLsfJUcc7GV2zCScRODBND7Y0i8m+xQjd0q7Bc/rE4ldsgBq4dFvuNPUS0hkzkPrpAKDp7hd/LF1Uge7VYIVr3v6RSjXVvbhR36ztUPgXuuiTKmrtF2bMrRbSGkkT6fn1PHM+qE3ZrD6sql/6BAZEye4bYpi4TR8y8xEjGRfF1SrUwnbdHGE7bIRYfShIj9YXiikuQ2ISTuOo+QYwWJYjNJ7OFfkS0MJq+U6kQU86ofEqdFyWG2hrE9E/ihHbwRmG5/GPxS3a96Dm2RxyUeIpoDZnIXjlftHb3iYBXLqnAPqehU7QffEW8pesq3tJ1FbW7N4ichk4VKAW8ckm0dveJ8udXi2gNJZn1/vyO+CW7Xtg/dkRoB28U0z+JE339AyJ1XpTYhJM4YuYlhlPOiN2XC4XZgr1CPyJabD6ZLUbkpSqgueISJEbqC8Wzp3KE4bQdwnTeHvHqxQIxnHZORfops2eI/gGl4tUO3ijs1h4Wx7PqRN+v76l8Knt2pWjr7hOBu5Q+Oa4/Lu7Wd6iqrrd0XVWqVPb0SRU4Nnf1iYroNSJaQ0mq3UffEqdyG4Tl8o+FdvBGEfm3WDHU3ijSF8wUm3AS52x8xVDSKfHm1SJhvvh9oR8RLZ4+cVcMN5arAO+yU4AYqcsXy75NVvl0X/H/aK4UH0lRkaqKRCdkk5A+ekj8mFkn+n77UOwzcBfb1GWidPPDorKlWwS9dllFfhm1HaL+7WfETi1n8bqOi2j5fLsoaOxSiQDfHRdEY2evyFu7UOzQdBafGXkoxVieXFiv+Ezohm4VU/fFiKEOhSpXTlv7iKE7J8TemyXCOGqX0AvbJjb8kiXGylPFDZ8QVa6M1uaKFd+lqCrgz+5UiIFrh8VxC2+xVU0pXAZ7u8Xkd26ofLpR2iz6Tn2kyv/ipx8S1a09D+R/cnWb6D761r+EDDz8AsWtsuY/dP0PZBAOXPuH/3fB/8Pem0dFeWUL348DoyCOUROTYgZxSEkITSROcWqySExMYqvLjpJgl1aCq7wkGILB8Emkg8HgokPCJ5HWDlEjQREFHAAlCI6IIIPMiMwgUFDF6Pp9fzxwEjK19/18b6/Vt/84i6p6ztlVnEqlkGEAACAASURBVL3Pnvc+kv/vzJ8rSdKl/wiD3xjGj9lS1dLJdc+lqEfIB7+3vZkFn6ULBlPf3kXW4gXCjdOt1wvG+0xgCr3364W2c9H1Odq79IKQ3D45T19DhTBDC/7sSWN7FzZbvhcHub/mNkcmy8Rasvk1qls7Ubx9BCMXFUsifuBB+TVhHVT4rudCWTPT18dg7Kpm24l8BgrSibRwwN/Qmnsf/4XEwgamrY7E1F1D0Nli+q4mEG5uzw4jaxr3bOVw7j3GLQvEbL4vYRlldH4TRIipLUHGNnSflk3nsYv9GbvYn/1XqmiL8ifI2IYQU1t60r8hLKMMs/m+TFkVzuHcezTu2SoOdd/VBILOFmPqrmHa6kgSCxu49/Ff8BlpSaSFAwMF6azcn42xq5rp62MoadRS4bse9QgFidNm8aD8mmA+irePUN3aScnm11CPkAVNf81tgRubLd/T+BPcpM5+lm69HrdPzgvG1N6lFwxlCDfPBKYI3HTr9QI3WYsXUD+IGwOll2zBdbSSbK0U1kFVS6cQPksifuBB2RUSp81CPUJBpd+bFDd2CNys3J/NQP55jj3mhM9IS+59/Bdu1bYL3GyIvUHf5eOEmdnhO9qKpr0aYnNqmPxKGGbzfWW3ZOpBQkxlDbktSsbHRM8Qxi725+tr1egT/0aQsQ2hY+zo+naXwM24ZYEcvVWL7rtP2WFkTbi5PfqEfQSmFAntOqmogZpAb/wNrQVutp3IF7i5UNZMhe96fEdbETPBkQfl13jrcI7AzfW7bRRvelVo+f33Cnk+NA1DZ29stnxPU4eO/HUvCgHV11jJH3adw0DphaMmAa1OtmhVkmzF9bY14rwjWQjBbl2XYPSXly6ip0srlAPnHcn0tjcLJSRn5TKqWjqFNbz8i0z6mqpJnDZLWCrFjR3YquMxdPZm1deX6a8t5sjkGWhGWVK86VVu3mtj2upIjFxUAjeHJjriO9qKcs1aSpsejZvIYdbTZJS3PNSQJKlKkqTrPxl/+Qkzf12SpOifvP+zJEl/+x1h8DdJknb85P3AIMzLkiS98r9eGJhMtUWn7xZEVbb1TyQWNjD5lTDGLQskpbiRhk/fxWekJWcdneltrmHO9tMYOnuz6POLDOSfJ3SMHTuMrOk+HUnohVKMXdVYbjpGaZOWy0sXoZJk10BRQwdTVoVj4uZDeGY5zfv+C39Daw5NdKSvoUIwuaf9T9PXWEnspBn4jLSkaa+G8MxyLJYEMGVVOEUNHZRsfk0wMK1Oj7UqDmNXNW8dzpF/xxg7QsfY0Z97hrcO52Do7M2s90/R3qXnrKOzEDznSpoYtyyQsYv9SSiop1yzFp+RliRbK+lrqBAa34LP0nlwJ5MwMzv8Da3RH9/LO3G3MHHz4SmvWFq0OqGZZi1eQHmzlmmrIzF2VROSVkJblD87jKxl66K2mBf2ZWCg9GJeSCq9rbUcmTwDPwMr6nar+epyFeOWBTLp5VDy6top16xFJSnIdH8efXc3Nlu+x9hVzdqDV+lJPchOYxuCTWzpu5rA8i8yMXT2xmnbSVq0OlJnP4t6hILCjS+RUd7C+BVBmC/04+itWir93kQzypJT02fT06UVQmLV15d5UH6NiLH2+BlYoTuym/dP3sbUXcP09THUt3dxc9VyVJKC/HUvUt3ayeNrojCb70tgShHagzvxHW1F1DgHBu7msfyLTAyUXvxh1zl625uJn+KE72jZAv379buYzfdlomcI1++2kbfGQwiuni7ZOjVyUbFyfza9mUcJMrYhyNiG3syjrNyfjZGLCoetJ+jpbCf9GTdUkoLqDzZytfo+Ez1DMJvvy6EbNdQEeuM72oqEqTPp6WjFNegsBkovPL68xEBVLlHjHNhpbIP24E4CU4owclHxxLpoqls7xW+6uWo5dW1dTF8fg6m7hvdP3qbr2134jrYiYqw9AxU3mBeSioHSC5ePz9B/r5DEabPQjLKk+oONHL1Vi/lCP8avCCKjvIWCP3uiHiG7ZZs6dDhtO4mhszdb4/Pou5pAsIkt4eb29KQeZO3Bqxi5qLBVx6Pv7ibT/XlUkoJyzVpu1bYz6eVQxi0LZP+VKup2q1GPkGMevffrheL2wr4M+muLiR7vwA4ja9qi/AlJK8HYVc201ZGUN2uFYnHdcykF9R085RWLiZsP78Tdou9qwqMRBrOf5lJly0ONf2IZPLQwkCRp/SDTN/rJZ08M/rUeFDo2/6uFwZjH7ehrqBCadW9WHAv3XsBA6cXCvRfozTxK6Bg7Ii0c6GuoEJrN+ydv0306Et/RViRbK+npaMVaFYeRi4rQC6VCG05TulLerGX6+hjGLvYno7yFsq1/QiXJDOpWbTvjlgUybXUkt+s6BJFX+K5n/5UqzBf68eSGQ+i7u0md/SyaUZbojuwmJK0EQ2dvlB8m0dvWSMwER4JNbOk+E43Hl5eE5tufe4aIsfaEmdkxcDeP50PTMFB6senoTXozDuNvaC1rbU3VOGw9gYmbDwFJhTTt1eA72ooUu7nUtXVhuekYZvN9SSyUtckh5lDSqGWiZwiTXwkju6pVCITiTa9y9FYt45YF8viaKFq0OhEIbo8OIDK7EmNXNTN9E+nW6zkyeQY7jKzRxe1h7cGrGDp747wjmQfl14ge70CwiS0PSrNZEvGDYNj9OcnsNLYhdpKslQ4FLzcfy6XjwEf4GViROG0WbZ067N89jombD7E5NTTu2SrcVTWtnUxZFS405SHGl7fGg5TiRiZ4BDNlVTg1rZ3CtdS4ZyuxOTWYuPlg/+5x2jp1JE6bhZ+BFR0HPmLzsVyh3fbfKyR20gx2GtvQn5PMqq8v/2hVlGYTbGJL9HgHHpRfw3lHMobO3qw9eBVd3B52GFnLAWm9npm+iRi7qonMrqQ9OkDERFq1Oh5fE8W4ZYEczr0nNPVrHi+QXdXK5FfCmOgZQkmjltw3/ohKUlAT6E1iYQNm832x3HSMurYuUuzmCuskIKkQQ2dvHLaeoK+pmoSpM+WzkXGYTUdvYqD04vnQNAbu5hFmZkfEWHv6c88Ii2z5F5l0n4nGz0C2KHrbGlF+mIShszfB5++gO7IbzShLUmc/i767myc3HMJ8oR/7r1RR4bteCP7bdR2MXxHEuGWB3KqV43VDCltGeQtjF/szfX0MpU1a0pSuwvoKvVCKkYsKa1UcPR2tQrvvPh3J+ydvY6D0wjXoLH2NlURaOBA6xo7ezKPi3C/4LJ3erDhhMf303D8KYeA4+2myq1ofajwKN5EkSUslSSqSJOmx34H1d0mSXv9fLQzMn3SgN+MwPiMtOefkwtXq+5i6a7BWxdFfc5udxjYcmuhIY3sX41cEMfmVMPTd3URaOBBiaktvay1PrIvGfKEfJY1aMtzm4TvaioGCdOaFpGLkoiKxsIGit1eiHqFAf3wvG2JvYKD0Ivj8HZr2alBJCmp3bSY8s1xokN3JUahHyH7Q1NImjF3V2L97nAfl1/A3tObYY05Ut3ZisSSAaasj6dG2EWZmR5iZHT1aObBqsSSA6tZO0pSu+Bta86D8Gi4fn8HYVc2Fsmby1nigHqGgOzmKlfuzMVB6EZFVwb2P/yIYevD5O+Lw64/vFX7XxMIGjFxUOG07yUBBOr6jZcZb0qhl0suhTF8fQ29rLTETHIm0cEDf3Y3i7SOMXxFEY7vMfHYa29Bfcxvlh0mYumu4Wn1fZAR1HPiIJRE/YOjszbYT+VT6vSmE5NCBfmFfBp3fBAnGfjj3HobO3rJldTWBncY2JFsryatrZ4JHME95xdJXX0akhQPR4x1o69QxfX0MEz1DaO/Sc2r6bIKMbehrqGCmbyJm832Jz68ja/ECWQh/9ykLPkvH0NmbwJQiSt95QzDXIQHgGZXF/cjtQljuv1KFiZsPzwSm0JP+Df6G1qTOfpbsqlbGLQvEWhXHwN08ws3tiZ00g/r2Lh5fE8WUVeFUNGuJn+JE6Bg7HpRm47D1BGMX+3OupImLrs/hZyAzOLdPzmPkoiIso4zCjS+hHqGg4dN3Wf+PaxgovVh78CqNe7aiHiG7KcMyyjB2VctustOR+BlYcdH1Oc6VNDF2sT8OW0/woOwKoWPsiJ/iRLeuiymrwpm2OpK6ti5iJ80g3NyegapcbLZ8z7hlgWRXtZI6+1n8Da3pST2Iy8dnMHHz4avLVUII3Y/cjmdUllBGhpSKks2vEXS2GCMXFQs+S0f33af4jrYia/EC4vPrMJvvy0zfRPoaKgg2seXU9NlodXpBZ61aHdHjHYi0cKC/rgTF20eY4BFMXl27oLO+y8d52l+26GNzaoTSoj24U1ip75+8LYRRpd+bbDuRj6GzN0sifqA38yg7jKwfkTBQcrX6/kONfyIMRkuSVCFJktVPAsgzfzZn7mCQ2e5nn48fshIkSZokSVLpz4PP/+uEwQSrGXQc+EiY16u+voyhszcRWRVcXroIf0Nr+nOSsdnyPZNeDqWurYtICwfipzjRotUxdrE/yg+TGCjKENkwX1+rFib4EGNtiXiPBZ+lY+LmQ3x+HamznyXYxJaBqlymr4/h8TVR6Lu7CTG15dBER0qbtJi4+eC07SQ96d8I5hJ0tlgcppLNr+Ez0hJd3B6eCUzBfKEfV6vvkzhtFjuMrOltlYOMU1aF09tcww4ja05Nn82lyhaMXFS4fHwG3XefopIUlL7zBm8dzhFCKmflMtQjFPRmHsVRk4Cpu4byZi0xExwJHWNHp07PuGWBPLnhEAOVN/EdbUWa0pW4vFqMXdUs3HtBCLq63WrhKjl0o4YMt3loRlnyoDQbxdtHGLvYn1atjnBze6LGOVDT2slEzxBs1fH0Xz+Fv6E1V1YsJjyzXGjOQ8JBe3Anbp+cx9DZm3MlTSRbK/E3tKavvgxbdTwTPUPo6WglapwDxx5zIq+unbGL/Xna/zTdpyPRjLKkcONLbDuRLxjCkJ+7OzmKhXsvYOyq5nZdB2lKV4KMbejpbGf6+himrY6kv7aY0DF2pNjNJaW4EVN3DfNCUoX2Xv3BRiFQI7IqKH3nDXxGWtKfk4zLx2cwm+9LXVsXidNmEW5uT4tWx5RV4VhuOkZvay07jKzJdH+eq9X3MXJR4fHlJXRxewRNDWnq8fl13Fy1XFZEqnJx2naS8SuCKG3Scmiio5yK3N3N+BVBsgCvysV3tBU3Vy0nPr9O0FRLxHuoJAW6uD14fHkJIxcVX1+rJtP9eXYYWTNQlIHlpmNMWRVOyyDOTk2fzb37XZjN95XdQznJqEfINBWRVSEsueoPNqIeoaDjwEfMC0nF1F0jp1DazZVdmrXFTFsdyZMbDtHT2U6QsQ1pSldu13Vg7KpmzvbTdCdHidjbkFKw7UQ+BX/2RDPKku7TkTztf5qxi/3Jq2vn2GNORI1zoEfbxkTPEKatjqSvvgx/Q2uSrZWcK2nC0Nkbt0/Ooz24UwiC1TFXMHT2JjyzXGSL6Y7sfiTCYMZsJdfvtj3UeIjU0hclSSoZZPgBg5/9P5IkvTz4+rwkSY0/TyGVJGmeJEn5gwIkX5Kktx8FP/2/xqglSTKWJOnq4A8ukCQpaPBzK0mSrkhybu1RSZIMBz83GnxfNvjc8p99x+N2M8lf9yI7jW3oba5h7GJ/Fu69QFuUP+oRCvrvFTJ9fQy26njBOPWJf8NaFcdEzxAGKm8KjdX9r2kYu6pp69Sxw8iai67P8cGpAoxcVKSWNhEzwZGzjs6kljZhoPQiLq+Wi67PETHWnpv32jB2VbPp6E3KNWtlrU+vZ6JnCC4fnxFCZaAgnSmrwuUDMygk2qMDcNp2Uib0pmoRMPP48hJm832paNYSYmrLdc+l4nDerusgYepMEqbOJDanRjCrZGslIaa2VDRrMZvvi8eXl8hf9yI+Iy3pa6rGYon8XUPMrif9G57ccAhbdTwDBenCynH5+AwTPUPo1uvxM5ADcJuO3sTYVU2rVkfEWHsuuj5HQFIhBkovsT8xExxJLW3CyEXFB6cKuOj6nOzf7dRh7KrG/a9pVPiux8/AioHKm0z0DOGZwBT0iX+T9yf/PLbqeKavj6H/XiHqEQraomScjl3sT29zDTuNbchf9yKeUVkYOntTPrg/ydZKIrMrhdBKmDqTcHN7Cuo7MFB6sf4f17juuZRgE1t6tG1ifxr3bJX3p7GSaasjcdp2UigYPakHmbP9NFNWhTNQlIFmlOzKcA06i6m7RuzP5aWL2HwsF2NXNdfvthFp4cCRyTMEow69UMo5Jxeixztw734XRi4q3j95m+JNr+JvaE17lyyc54WkCkE5UHEDxdtHsNx0TOyP7shu7N89zvgVQWJ/Sja/xqLPLzJ2sT/17V3sNLYhb40HwefvYOgsu5RiJ80gSTGH7KpWsT+XFswnzMyOooYOTN01rD14VdSt9HS0Yr7Qj6f9TwuloO9qAo+vieLxNVH0XU1AJSlo3vdfPO1/msmvhNHb3oxmlCU1gd4ihVff3U2YmR1Zixfw9+t3MVB6cbX6PkmKORyZPIOEgnoMnb0JSSvhnJMLQcY21LXJ+7Po84tif/prixm/Igj7d4/T9e0uWdifjsRy0zEsNx3jQfk1VJKCrm93MS8kVd6f2mL8Da0p3vSqUBYehTBwmqPkVm37Q41H8X3/k+P/pjAYIUmS2eBrg0EG7yZJ0neSJK0Z/PwrSZK2DL5WS5L01eDrNdJPcmp/ayhmzCZ19rPET3ESTLq8WUvEWHtyVi5j4d4LmLpr6GusFNqYzZbvcdh6QrhyetsaGb8iiOVfZFLhu56dxjYU1Hdg5KIiMlsOBB97zInwzHKMXFQUN3aIApwX9mUwwSOY/pxkmXmcO4D9u8exVceLQ9TbXIOpu4YX9mVwzeMFosY5UNKoxUDpRUZ5C8cecyJN6cqG2BsYuajQ6bsJMrahwnc9T/ufZvyKIMGou09HMm11JK5BZ7kfuV32E3foMHXXsO1EPil2c4kYa09iYYNg0mFmdpxzchE1Ce1dejSjZFeO845knlgXjf74XlSSggdlV3h8TRTPBKZQsvk1NKMsaevUYegsu1LOObmQMHUm50rkva5q6ZSF4arlLPgsHbP5vvTVl+E72or7kduxVsUxbXUk3acjUY9QMFCUwbhlgXhGZVGuWUuQsQ15de0YuajYf6WKQxMdiZ/iRFhGGQZKL0qbtOwwsua651IWfX6RiZ4h9DVVy3ud/g226njs3z1Ow6fvyri8X4+Jmw9LIn7gyorF7DS2oaihQwjLI5NncNH1OSKzKzFyUdGt14vCsDnbT8u4zD0j7/WZaKa+HoHNlu9p3vdf+I62oq+pWgRfr3sula3MQYZ/oayZ0DF2pCldRdZOp07e63LNWpQfJjF9fQwDRRmC2U9bHYnLx2co3vQqvqOtaNHqRBD2rKMz4eb2JBXJuEwpbiTc3J68NR5sPpaL2Xxf+muL0YyypD06AMtNx2QLNWEf6hEKHpRmM25ZIM47kil95w2CTWxp79Jj6OzN36/fJXX2syRMnUlIWgkGSi8qmrX4G1qLczPp5VD6GirEubFWxeGoSfjFufH48hJZixcQNHhuDJRefHW5ithJM8hwmyfOTUmjjMu7AW/9eG6unxJCd8qqcOzfPU7jnq34GVjR21wjYmA/PzeXKuVzk/6MG+v/cQ0jFxX67m58RlqKc/PkhkMM5J8XVqLD1hOPTBjk1bU/1PiPMPh1wWAqSVKOJEl/kCSpRZKk0YOfiyCKJElnJEl6bvD16MF5I34Prt3Mp4mZ4Ej+uhdZ/kUm01ZHCq23qUMnTPBgE1saPn0X5YdJ2L97nLKtf8Lf0Jrrd9swUHpR3NiB72g5n1/x9hH+sOsclX5vEmZmJ9xG+u5uYYJPejmUtQevcs3jBY495iS0wv6a2zLTK5AZ48r92aTYzeWi63O8f/I2FksChEXQ21orgnLR4x0o3PgSL+zLkC2Ey8dRj1BQ394l4hZBgxrf0/6ncdh6Au3BnfgbWgttr7RJi2aUJbW7NvOUVyw2W76nwnc94eb23Kptx0DpRbeuC9/RVuiP72WiZwiuQWe5smIx8VOcOHRDPnydOr3MrIoymPxKGKu+vkyytZIjk2ewNT6PccsC6a8tlv+HtkbMF/oRklZC1DgH0pSuLPr8IhZLAujNihMWiZGLipTiRoGrWe+fYurrEbRHB7DDyJoWrU4wpGATW+p2q1F+mIStOv43caVP/JvA1eWliwgzs+Pv1+9i4uaDTj+Iq+8+Fbi67rmUY4858U7cLYGrIWtkKE13CFcL916Q/4eMw0KgGzp7E3S2mJgJjhT82ZMlET8wbXWksEIb27swUHqRUFBPsIktuW/88Re4ulp9HwOlFyWNWmFlPOUVi9sn5wWu9l+pknGl1w/DlcvHZ7jm8QLxU5xEAHygKlfgymy+r8BVhts8tp3IH46r+/UCV9Hjf8TV42uiBK6GtPKkoh/pbc7200x9PYKOAx8Nw1X5T3A1ZF2Wa9YKS9lA6UVPl1b+HxL2Ybnp2O/i6sGdTCa9HMrqmCskKeYIXA1ZQSpJQX/uGcwX+hF6oZQUu7mkP+P2I64yj+Iz0pLe5hqMXFScK2kahquxi/0fmTC4XdfxUOM/wmC4EBg16OvqkiTp08FgR9lPnj8pSdLtwde3JUma/pNn5ZIkTfoVmH+RBvN2H5/+JL6jrXhQdgWz+b5cKGvGZ6QlPakHMXZVU1DfIbsk8s8Lk1kzypKBu3mYumu4frcN9QgFfZePY+Si4nDuvd+FV9QwHF5SkQyvv0ZOXbx5r40dRtYC3tFbtcPgZZS3/Dq8gvRh8PSJf8PEzedHeFcThsMrvybgDbl7jF3VFDf+CG/sYn+SihrwGSn7Yk3cfLhV2/678C5Vtsh+9UF4f79+dxi8lOLG34UXlyfD0333qYC389fgFWVgvtDvR3jJUQKev6E1/ddPYbEkgLi8Wjn4G7dnGLzejMMPBS8yu3IYvPj8OgHP1F1DdlXrMHiHbtQMg3eupEnO8von8PTH9w6Hl3mU8SuCBLzOb4KGwzsTPRxeTvKvwgu9UPoLeL6jf4SXWjoc3leXq4bBSyioJ3SM3W/Ci82R4XV9u+t34bVF+Q+Hl7BvOLysOMYtC/xVeEHGNvScO8BEz5Bh8IbSoUNMbQW8sIwyOV42CG/oPHZ9u0uk/g7Bm+ARzP4rMrz26ADGLvYnsbDhV+E17dXwlFfsI2HOM+coKajveKjxH2Hw60JhnCRJ6ZIkPf//Vxj8dMydK1eZljbJ5mNP6kEiLRz44FQBNlu+J8VuLldWLGbq6xEczr3HTmMbBipvCk3fZ6QlX12Wi7SqP9hI1DgH3D45zwv7MkhSzKFpr9xLJj6/Tk5LHNT++nPPEDrGjvDMcp7ccIiLrs9x0fU5ntxwiOVfZBJiakt/rpyR0dShQyUpuHe/C1N3DU17NYSZ2bHo84u4fXKeNKUr1R9sFEViPiMtGSiSMyQGKm6gHqEgNqdGzgbZrSbFbi42W77H/a9pRIy1pyf9G8wX+lHerJWDsjo9xq5qHpRmE2Rsw+oY2fWTtXgBxZtexXLTMYLP38F3tBW9WXJV6ZA74FyJnPnUHh1AwtSZeEZl8UxgCocmOlK29U+MWxbIzXttsl9Z24ahs7fwzb4Td4vJr4Rxc9VyTk2fjaMmgXfibhE9Xm6BYOjsze26DjmYfq9NNu2P7+XQREfeOpzDTN9EEqbKMaCJniG8f/I2fgZW9DVUYOjsTW9bI76jrciuamX8iiCKN71K7KQZKD9MYtXXlzn2mBPagzsxdlWTUd4iNHoDpRc96d/gO9qKgKRCbLZ8T+HGl7iyYjHTVkey/h/XZLqouDHM3VDc2MHYxf50n4kWdGH/7nGSFHNo3LOVKavCicurlTXzu3mCLnxGWhKRVYHFkgAqfNdz0fU5XIPOsvyLTELH2NES8R4mbj40tnfJAfo2mS76riYQZmbHC/syULx9hDSlK5nuzzN9fQxfXa6SExYG6aJVq0M9QsHh3HtyEHuQLhZ8lo77X9OItHDg3sd/wXyhH3+/fhf1CJkuDJRePCi7QpCxDX+/flfQxTknF6xVcQSdLSbc3J7ezKPCDaiSFLRqdYIugk1s8YzKwnlHMpnuz1P6zhuMWxZI6IVSEW8wUHoJukgsbGDSy6G0RLxH4rRZOGoScA06K+jCYkmAoIueLi2Gzt7oj+9lh5E1m47exGnbSa57LiVvjQf27x5n24l8OSZ3JlrQhUpScLX6PuNXBMmV+IN0MWf7aY495kThxpew2fL9IxMGRQ0dDzX+Iwx+WyAESpL0/qN0Ezk/PRufkZZsO5HP9PUxJE6bRc+5Axi7qulrqibYxJat8Xm4Bp2VA4fnDsgm+JloQkxtcQ06y7YT+QSb2ArG0X0mmsRps3D5+AzbTuTL/tHmGoxd1VT4ridx2ixR4akeoaC4sYNJL4dS4buevDUe4plK+vFZ+jNu5K3xwHyhn3DDRGZX4rTtJGlKVzoOfISB0gudvlvOjx58FmnhIJ4NFKTjb2jNkogfWLk/m0gLBx6UyVkTAwXpxExwxP7d46zcny20fUNnb1oi3hPP/n79Lr6jrWjV6rBYEkBLxHtkuj/PBI9g/n79rjj4FksCuO65VDwbYgpJRQ085RXLdc+l1AR6Y+SioqqlE5+Rlmw+lstTXrHET3ESz/rrSvAZacmmozeZF5IqFxJlHJaZRV0JoWPscN6RzLyQVLkYq61RFtSJfxPPhrTB3rZGTNx80CfsI9layeNroghMKUI9QsHtug5M3HwofecNCje+xONrosiuahXPpqwKF8/M5vtytfo+KkkhGqSdc3IR2me3rosdRtaEXihl1vunxLMhN9sOI2sW7r3ArPdPycVaVXJa6lAriIV7L7D24FWRvmno7E3/9VMcmuiIzZbvWXvwKppRljS2d2Ho7E3jnq1kLV6AzZbvOZx7D5UkuwfHkkHLOwAAIABJREFULQsUz8avCOLoLVnwxOXJ9R9XViymbrea8SuCuHe/C80oS+LyarHcdIwjk2dQt1uNkYuKujb52YbYG1ir4jgyeYawXPtrbhNubs/6f1xjwWfpchrnVbkKWPfdp4Sb2/O0/2kWfJbODiNrejrbxbOzjs7M2X6akLQSmZF3tmPqrqHo7ZWcdXRm6usR3Kptl4vA7raJZyWbXxPP1CPkZ9NWR5JiN5eSza9h6q6hRytb7EFni1F+mESK3Vxh3fR0yNbX0LMwM9nyMVB60ZsVx05jG54PTeOtwzmEmdnRf09Ocujp+P28/4cWBk8rKW7seKjxH2HwI/OfLEnSuMHXJpIk/SBJkqckScd+FkBWD75+52cB5O/+2Xc4O8pVsRM9Q8irk10Wtup4IrMrUY9QsOCzdDyjslBJCgKS5MIm39FWnCtpYtrqSJEOaL7Qj2RrJV3f7sLQ2ZvcN/5I8aZXMVB60RLxHmlKV6G9RY93YNLLofS2N+NvaI39u8cpaZS18oV7L+Dx5SVUkkzIM30T8TOwIqO8hamvRxAx1p62Th1m833lPkCDjCR/3YuCoNui/Dnn5IKpu4b+3DNEjXNgyqpwerRt+Bta46hJoKhB1qRe2JfB/itVqCQFIWklrI65IuIIU1aFE2nhgFanx9Rdw1lHZwYq5BqJwo0vofvuUwyUXnQc+IiyrX/CbL4vAwXpRIy1Z+rrEfR0afEzsMJp20mhuS3/IpPI7ErBSFfuzxb+/DnbTxM1zgGdvhtTdw2ps5/lQalc/1C86VWxt53fBFGy+TXMF/rx4E4maUpXOcis1xM93kE0TlNJCjyjsgjPLJc7uGZV4PHlJXYYWXOrtp2ZvolEj3egW69n6usRpCldhe+89J030B7ciaGzN7ojuyl6e6X8fYO9cB5fE0WnTk/UOAfmbD/N1er7+Btas3J/NqEXSlFJCr66XMWSiB/YaWzD7boOnLad5NBER3o625myKpyLrs+JeEPZ1j/RHh0gM8q4PRT82ZOxi/0ZqLjBWUdnnlgXTXuXnkgLB5QfJnGpsgU/AytWx1wh+PwdVJKCr69Vs+jzi6gkBUUNcgPF2Ekz6OmQi88y3Z+nPycZU3cNFb7ruR+5XRacCfvIX/eirDDczSPFbi5m831F1pfzjmQulDXjZ2DF+n9cIzClCJWk4NCNGhbuvSCnkTZpsX/3uOyiaWtk0suhRI93oO9qAiZuPlR/sJHmff8lC8TTkeS+8UdZyN0rJEkxB/OFfjR1yKmqQ/2CfEdbMWf7aT44VYBKkq2Y50PT5LYszVpstnwvUqgneobIBW5ZcZi4+XDR9Tka92zFQOlF2dY/kbNymfx9dSWcmj5bZE6FmdnxxLpo4V592v80207ko5IUrI65wryQVDlFeLCm51Ew51lPz5X7gj3E+I8w+FEYzJEk6aYkt1+9LUlS4ODn1pKcclo2KBiGiieMB9+XDT63/mffMdfqcS4vXSQOYeOerXLGz6ZX6c9JZoJHMCl2c+nWy/2GwszsyChvwePLS/iMtOT9k7cFM3UNOkt4Zjl+BlZMXx9DUUMHUeMcMF/oR2+rnEZqoPSi59wB7ga8haGzN5V+b9KT/g0WSwLkpnRtjYxdLHfevF3XwZMbDsmdTS+U4vaJnNmw4LN0ApIK0YyyFJ0/w8zsGLcskJ4uLWcdnZngEUzf1QRKNr8ma3e71egT9mGg9CJr8QL66sswdddwaKIjFYOtI/wNrfn6WjXOO5LF4dt09CbqEfLBSCpqINjElpm+iaLqdvIrYTy4k0neGg85rTbKn5yVyzBQepGzchn3I7dj7KomfoqTCPAFGdvQ3qXHadtJuedQcSOrvr6MSlKw+VgusTk1qCQFzjuSuXe/C39Dax5fE0V/zW1iJjhi6q5Bd2Q3lxbMF4KpdtdmOXCpmEPfZTl18pyTCz2d7diq4wk3t+dq9X1e2CendwamFBGRJbu25oWkUtIoByqf8oqlr6maSAsHORW1vZk0pausNWYcpsJ3PYbO3twNeIvuM9FyVbnbPHqba3jKK5aocQ4UN3YwLyQVPwMrwjPLcQ06i0pSsOjzi2w7kY/PSEtstnxPRnkLoWPsGL8iCH233BLF0Nmb/twzFL29EiMXFU17NULoXlmxmP57hXIl9aQZ1LR2MvX1CHYYWXPohtwmRSUpWLk/mw2xsntw/T+uEZ9fR5CxDXO2n6apQ0fC1JlMWRXOg/Jr3Fy1HBM3HzoOfCSEUd4aDx6Uyv2jEqfNYiD/PBM9Qwg2kVu3OGoSUI9QcKGsWShK207k8/W1avwNrXH5+AxVLZ0cmujIE+ui6a+TA85m833RH99Lhts8OQi++TVqAr0xdPYmxW4uvZlHGbcskDAzO3rbm7FWxaEZZcmt2nYW7r2A72grgs/fISyjDJWk4PnQNArqO9CMskTx9hF679cTbm4vJ1mkHuSckwuGzt5Uf7CRsq1/wkDpRe2uzXSfjsR8oZ/cVr6hgic3HCJmgiPlzXIDO39Da766XCWE0PIvMnkn7hYqSfHIhEF5s/ahxn+Ewf/gmD1lIu3RAXJwbaIjSUUNRGRVoB6hkCt7zx0g940/ymmiFg60anWCoSg/TKK3uUZYAGlKVx6UXcFWHY9KUrDp6E2KGztIsZsrtLH+2mImeATjO9pKdtVkVxJmZoexq5ruM3KK5tB3/f36XZZE/IBmlKWcptdUTcnm1zBx8yFN6UpVSydrD15FJSlw1CQwcFdOJzRQelGuWUt/XYmwZDyjsqhr6yLc3F4OjP/ZE33CPqasCsdnpCVfX6vm6K1aosc7MNEzhK5vd1H6zhuYuPmIuoO1B6/iZ2CFw9YTDFTlUhPojYHSi1PTZ5NX1y6YkceXl2hs7yLT/XmMXFTkr3uRno5W8V3Ph6YRlyd/15Cm35t5lOnrYwgxtSX4/B3W/0OutLZYEsBA5U3hvz41fTa3att5/6ScyaN4+wgD+efJdH8eA6UXRybPoEfbhmvQWdQjFLj/NQ19dzcxExwxdPaWO6ZmxTF9fYywvjLKW4if4oTFkgCa9mpE8HeHkWyxfHCqgCBjGxRvH6E/J1lo1LGTZpBS3Mgfdp1DPULB42uiRDtxQ2dvLi9dRHuXnic3HBLC7VKl/F0GSi8a92zlwZ1MoeFuPpZLQX0HQcY2ctrn9VO0RfnLgdpJM0gqahBWzhBt3ly1XNBLW6eORZ9fxGekpdzIrbWWhKkzMVB6kf6MGw/KrmCz5ftfpc1KvzdFLv4QbUZkVRBmZidbXT+jzUM3asQ5mPRyKL3NNb+gzdUxVwRtljRqBW1W+K6nv66Emb6Jv0qbhRtfQp+wj8mvhA2jTc0oSyZ6hqA7slvQZugYu2G06ahJYKAql7sBb2Gg9CJJMYe8unZmvX9K0GZ9excZbvPEOehtbxa0ueCz9GG02fXtLnozj2Li5sM5J5dHJgwqmrUPNf4jDP4Hh73pGNo6dey/UkWIqa1cgWtiy1eXq9Dq9NyP3I75Qj9UkoLJr4TRk3qQvoYKNh+TKzgNnb05NNGRS5Ut3K7rIH6KE0YuKjSjLFkdc4WBqlz6r59i2mo5T97UXUPdbjV1bV0czr1HmJkdBkovdhrbEHqhlG6d3IXTYkkAKkmB8sMk9Il/o/d+PTN9E/E3tBZad0pxI6VNWk5Nn42xqxqfkZYs/yKTB3cyqf5gI9PXx6AeoeDJDYeo/mAjFc1a2U1j4SCYeEBSIb3tzVz3XMoEj2BUkoJZ759Cd2Q3+oR9KD9MYqexDeOWBXJ56SLi8moJSSsRTCTc3J6Fey/Qn5NM7a7NKN4+Ihhj2dY/MVB5k1VfXyZmgiNGLirip8jpfhnlLeSt8WDSy6FCwLRHB9Bz7oDQpIe0bq1OT2R2pdDQQ0xtmReSSqtWR/O+/xLCd8qqcAo3vkR/bTFvHc4R+ImdNIPrd9u4VdtO8aZXBX5stnxPf81terPicP9rGipJgdl8X1JnP0tTh45DN2oIHSPjJ8jYhvDMcnT6bjoOfMTYxf6oJIVcWJccRW9zDVvj8/AzsMJA6UX0eAdSS5sobpSL+4bw4xmVxYPya/TnnuGJddGoRygwcfOhJtCb6tZO4vPriBhrj4HSi2RrJUFni+nRtnFlxWLGr5Bbb8zZfhpd3B56OlqZs/00O4ysheWQUFBPVUsnydZKTNx8iBhrzwv7MhgoSKcm0JunvGLxGWnJE+uiqfBdT0mjFs+oLKLGyTSROG2WfFlQay03Vy1nomeIcPVpD+6kO1muIQkytsFiSQCZ7s8Tm1NDeGY555xc5BRbMzueD02j77Kc82+tikMlKZj6eoRscdfcZv0/rnFookwTRybPYNPRm1ytvk/hxpeY/EoYvqOtsFXH0xLxHr0Zh4VVbL7Qj/Rn3GjV6vj6WrWwtoNNbPnDrnNodXraovyxf/e4OLP5614UZ9bPQKaJmAnymS2o76D0nTcETVhuOsZA5U36r59iwWfpw85sf07yI2HOs5+eS1VL50ONf0thIMnVweskSfpwMBAcOOT2+VcOq9HGRGZXMlBxg65vd+Hx5SVhXqokmRBy3/gjgSlF9OeeoTs5ivX/uEaG2zyMXFSoJAXGrmqyFi9g87FcWrQ6erPi2HYin3Bze0zcfFBJsiaX/owbq76+TLdez0BRBqEXSomZ4CiEzaSXQ0m2VpJU1EBvay39Nbf5w65zHHtM1lp9RloyblmgyOnvv1dIb7Pst01SzGGiZ4g4MIUbXyIso4wHpdnovvuUlfuzSZ397LD/K2flMtGXv+fcATYdvcmlBfMxdlWjkhRMXx9DpvvzvHU4h/YuPf3XTxGQVEikhQOm7hrBgFNnP4tnVBY9ne08KLuC8w45EGq+0A/NKEsmeARzavps4vPr6Guqpr+2mHkhqSRMncm4ZYGoRyiwWBLAkckz2H+lioG7eXQc+IgX9mWQYjcXA6WXYNT5614kJK2EgYJ09An7WB1zhfRn3AQuTNx8uObxAttO5FPf3kVvxmHeibslrC+VJAuqDLd5rD14lU6dnoH88wSdLSZqnOzKGGIiQ9Xive3NDFTexOXjM8ROmsHYxf74jLRk/IogEqfN4uitWvrqy+hrqOD50DQSp81igkcw6hGyQCve9OowGlv+RSZnHZ1/QWNBZ4tlGjstZyiFmNqK/+vJDYdElfJ/h8ae9j8tXDS+o62G09j9egbu5v0+jdUWiwruJMUcDJReqEf8SGPhmeWCxjyjsn6TxmpaO+k5d4C3DucQOsZuGI1dWjCftw7n0Napo//6KT44VfCrNJZR3iJoTPlh0jAam+gZQpJiDgkF9YLG3D45T/wUp2E0Vrb1T3x9rXoYjSVbK3+dxooyBI0FGdtg5KJ6ZMKgurXzoca/qzBIkeRWEX6SJPkOjX/1j398hBHKD5PIdH+ery5XoYvbw/4rVej03fR0aenU6en8JojwzHI6vwlizvbTpCldmfX+KXYa2zB2sb/QzFSSggkewYSOsWOCRzBXVizmD7vOiSZq3WeiOXqrlqYOHb3362nq0NES8R6BKUW0RLyHw9YTInjnb2gtDsMQ7EkvhxJubs/kV8LIfeOPLPgsndw3/igf7Kw4UoobqW7tpK++jK3xedTtVrM1Po9T02djrYojcdosTNx88B1thYmbD+oRCgydvVGPkLW26PEOTFsdSeHGl1j+RSb9uWe4VNnCQP55BqpyKW7sYKAqlw2xN6j0e5P1/7jGsceceMorliOTZ2DkosJnpOWwv7GTZjB9fQyxk2aw6uvLlGvWsjrmCg/KrnC7roMH5dd4cCeTm/faGCjKwDMqi5LNr/H4mihiJjjy+JqoYb/T2FWNZpSliEMo3j5CwtSZovHZpqM36b9XSHmzlgtlzfRfP0VqaRP5615k0ecXyV/3ogiMT1kVPmx/Td01+BlYYequIUkxB1t1PI17tvL+yds07tlKXVsXfU3V1LV1kVBQT0/6N8Tn15GzchnzQlK57rmUiZ4hhJnZCcFsoPTC39Aas/m+7DCyxmnbSc45uYiWHiFpJbRHB9DWqaOno5W2Ttki0Sf+DZePz5C1eAEuH58hxNSWccsCCTaxFb93yGKxWBJAkLENT/ufJsNtHsoPk9Ad2c1Xl6vQd3fTrdej7+6m69tdRGRV0PXtLp72P036M27M2X5a0PFOY5thsENMbRm/IojLSxfhGnSWy0sXEZtTQ/fpSA7n3qNVq6O3vZkWrU6+8+JsMfcjt+OoSeCsozOOmgTxv/8aHU96OZSclct4PjSNm6uWk1jYQG/GYZKKGrh3v4u+hgpqWjtp+PRdtp3I59T02dhs+Z5T02f/Lh1HjXNg6usRolhsqH15f04y/TW3KWnUMnA3j7cO53A34C02xN4gfopMx8cecxpGY0OwD010ZOX+7EcmDGpaOx9q/LsKg9v/6h/6a2OSZIihszc+Iy0xUHoRZmaHy8dnSJ39LAFJhfTXlXCrtp2SRi11u9WEXiglw22efDOUhcOwtUPBqzSlK4EpRdwNeIu8unaKGzvoba4Ra+eFpIq1mlG/XGuz5Xuxtu/ycXqbawhJK0GfsI95IalEjLVn+voYNKMshRY2blkg6c+4YauOpybQG48vL9F3NYG+pmp0+m70Cftw++Q8hRtf4ol10WhGWQphM25ZIP6GchbVvY//It9edv2UfPuYTo/++F6Sihoo+LMnT6yT02Z/Kqh2GMkZUUcmz8AzKoubq5Zz9FYtnTo9LVodiYXy2rUHr5IwdSam7hrBeHYYWWPi5sOxx5xYuT+b3Df+yNFbtXR9u4tWrY6EgnoGCtLFWkdNwjCmNbR22mo5QyUu78e1/bXFDBSkszrmCg2fvivWDrnDTNx88BlpybTVkeSt8ZAb9x3ZLWuodSUM5J8XeHfYekLgXSUpBN6nrY4kzMwO16Cz6L77VL7Vrb6M/twzFDd2PBTNuAadFTTTV182jGZC0kq46PqcwPsQzRg6exNubs+4ZYFyA72zxdQEynUYRQ0d9DVVC5px++Q8EWPtBd5/i2YK6jtkmmmsJPj8HfTH9/63aaavsVLQzB92nRM0M4T3n9PMEN77c5I5eqsWrU6PLm6PoJnH10Q9FM3E5clrh2gmf92LrI658ou1Q3iPn+Ik8B6XVyvwHp9fx0D+eVbHXCF+itMwmom0cHgkzHmOUm4L/zDj31UY/L+SJM3+V//Yn4/JkiGm7hry173I1vg8alo7uebxAg5bTwg/Z5JiDinFjRzOvUfTXg0TPUOELzF/3Yu0deq4d7/rF+tOTZ89bN2kl0OHrWvV/rhuyGz+6bqjt2p/d911z6XD1nUc+Og31+Wt8XiodfFTnB5q3ZRV4XQc+IikooZHtm7W+6fEurq2X65rjw74p+uGmsn9d9a1/GzdTmObYevi8n573YbYG7+6bqDixj9dl7Ny2UOvG7oGdGidiZvPb64bcuX8n66b6ZvIgzuZv7muLcqfgcqbxOX9GKD+6bqmDnldsIntr66Lz6976HWrvr78q+tUkrxuoCiDpg4dbx3O+c11yg+THsk6EzefRyYM6tu7Hmr8WwkDSW6PmidJUqEkSf2SJN0ZfJ8vSVLev/rHD7mJqgfNUUNnb7n74dliQtJKhCZVt1uNLm6PSLtce/AqAxU3uObxgjCpL1W2UNKoFWmJ6c+40dcoX6rtM9JSXMPYceAjkba3+VguX1+rFoHHcs1a8uraRUBz/Iog+q+fonDjS0x9PYKdxjbE59dR3y73lTd118iX63RpsVbF4TvaCsXbR7gb8Bb6xL8JE33l/mwSCxu4uWq5qEs4V9LE8i8yRZyh+0w0lX5v8pRXLH4GVuJWqSGz/NhjTrRqdRzOvccOIznds3jTq/RmxYmA99DFICWbXxMukticGmGKm7j50PXtLnT6bmzV8fiMtOTJDYfovV9P6uxnMV/oR/R4B5Z/kSlaGxgovbi5ajkPSrNFCqrLx2fQJ+wTqYm+o+U0zoCkQlLs5goXVX17l3CFTH09gv6a2/TnJDN+RRBhZnbMC0klr66dcHM5aHtpwXz6a4t5J+6WcEt1HPiIloj3cNQkiHTioUviDZReRFrI6aSuQWcJHSO7iAaKMhiouMHkV8JEIkB1ayd1u9WCxoLP3yH4/J3hNPbdpyg/TBJxiyEam+gZQugYO7KrWilu7CDSwgGLJQFcdH1uGI05ahJo69QJGlOPUAgaG0rFHaKxeSGpP9JYTjIFf/YUNJZQUC9ozMhFJdNYZzvhmeX4jrbCctMxagK9/ymNpZbKNBY9Xk6x7jl3gN779Ty54ZCgMZ3+RxqLn/IjjQ2lFJdsfo3erDgWfJYuanF+SmM7jKw5nHuPDbE3OPaYTGO6I7vRd/+SxrrPRP8+jd3JZOX+bJEqrk/8G3cD3sJy0zE0oywfCXN+WjmXxvauhxr/bsJA8XvjX/3jHcaY0dOlFVkNuu8+xdDZm9J33iBr8QKmrY4UV1su+vwiFYMtGxILG0RZe29rLQ5bT5A4bRZVLZ2YL/TjbsBb9Oeekcv3y69Rsvk1xi0L5HZdB7GTZjBn+2k2xN6QL6NPK8FaFSea47l9cp5IC/nSnUkvh3Jz1XJRFNRz7gDR4x0wcfOhrVNuKaB4+wjhmeX4jJQzmIYE1s17bYxfESS3Shhsd5A6+1lqd23GbL4vLRHvCd94X6Pc42bzsVxSS5tQSQrcPjkvWmMculHDE+uiyXCbR7dObkbW+U0Qurg9GDp709PRStbiBTLDzT1DmJkdC/deGLZf75+8LbewuF+Pw9YTJEydSceBjzBf6Ef1BxsFc31Qfo3Sd97AYkkABfUdHJks79dbh3N+sV+N7XIrhkgLB9ECIWflMrFf3WeixX61d+lRj1DwlFcsEVkVw/YrZoIj1+/K+1W48SWxX+ecXKjdtRlTdw3N+/6LJMUcbLZ8T19DxS/2q7ixg+VfZIpb2X6+X9qDO9Ef3yu3QGhv5vLSRUx9PYL4/DqxX0MML6Gg/sf9amvEUZMwbL8q/d78cb/KrhBsYiv2aygjbNPRm2hGWRJ8/g42W74XjQtN3TVEjLUftl8Ff/Zkgkcw3clRRI934JnAFNq79PiMtOQpr1gisyvxGWnJqq8v80xgCjETHEX7hoI/e9Jfc1tU8NbtVmPqrqG+vYtka6W8X4N3CGw6elNU1xc1dODx5aVh+3XR9Tkq/d4U+5UwdaZoGfHz/VrwWbrYr/j8Oqa+HiH2y9DZm4SpM9Ee3ImRi4pKvzdFfcPQDXMWSwJE8eXQfvkbWhN0thibLd+TbK0UrT4ixtqTUtz4yIRBU4fuoca/lTD4iVD4x8N89j89np7+GKXvvIHTtpNklMtNzIYqFyuataKXzVCrhzAzO2Jz5NYS1z2XEjtpBoEpRXh8eYk0pSt9VxOwWBLA1vg8VJKC/prbmLj58OCOrIH3aNvkQ3cmWuTcP7EumrYofwo3vsSc7adJKW4k2MSWex//BQOll2hGl1fXLvrKhJjaEp9fh5GLirw1HkSPdyAso4xFn18kw20ePenfMH5FEG8dzkElKUQjuYGKG6gkuQPluGWB9GYeRT1CrkSe+noEXd/uIveNP/JMYApxebWEjrGj0u9NDJReogncUBvgurYugoxtxAUhRW+vJNLCgSfWReP+1zSyFi+gOzmKiZ4hIud8oCAd84V+ootkX0MFYxf703/9FOoRcsXw5FfCODJ5Btc8XuAPu85x6EYN4eb2lL7zhqy9Dzabq2rpFHjaaWwj8FS29U+Em9vzlFcsrkFnue65FF3cHia/EobHl5fkeyoGG8UNtSbvr7mN+UI/0R567cGrTPAIJn6KE1mLF/B8aBqR2fI1iYUbX5ILpQbxVD/Yb6q4sQN/Q2vR4bX6g42EmNpirYrDeUcyeWs86PwmiKmvR7Do84tym5KMw4xfEST64zwov4apu4aBihskTpvFpqM3GbcskFPTZ3PR9TkWfX5RZKEN1b/E59cROsaOtk6dwJOfgRXFjR1YbjpG7a7NBJvYYv/uceZsP03hxpdoi/IXeBpqzDfRM4SeTrn9w0BRBiZuPvTX3CZ+ihPbTuRjsSSAFLu5pM5+Fo8vLxGQJF/pOeQmG8KTvrtb4EkzypKqlk6e3HCIpr2y795p20kcNQmUvvMGjXu28pRXLH/YdU50WJ38Shg6fbfAk7Grmr6GCnHWzBf6kaZ0JcVuLqtjrrA1Pk/gaag5XqSFAz2d7eLSI5+RltS3d4mz5m9ozZztp7FVx1Pp9yb3Pv4LlpuO4bwjWTQGnPp6hOjhFJ9fh/OOR5Na+vTcubRodQ81/l2FQc7P3o+SJKnwX/3jnQcb1eWt8RAXrQefvyMulB8oSMfYVY2tOp5uvV7MvVp9X8xti/KX5xZl/Orc7KpWcTH8/cjt8n3ERRks+CxdzD01ffbDzdV1cWr6bPLXvUh2VSsTPILF3KEGdT+dmzhtFvnrXuRSpXwZfEhaCS0R7+Ez0pIHdzLFnchDczWjLMXc0Aulvzq3p0v7i7kTPUP+j+YmWyvF3Bf2ZYi5Q5fXT/QMoXnff4m57n/95VztwZ2MXxHEpJdDuVXb/qtzE6bOxHe01a/OTbGbK5qwLYn4gf7rp34xN6/u9+d2fhPEB6cKxNzrnktJsZtLe9d/b67lpmO/Ofd2XcewuYq3j7D8i0xxefzQXCMXlZh71tGZtk7dL+Z2fbuL90/eHjZXJSnEXBM3n1+dW1DfIa6MHDb38nGCTWyJGGsv5jpqEsTcVu2vzx26WvKnc4s3vSrmRmZX/mKu07aTw+YOXVofmV1JkLGN3O+p8qaY29ve/PBzB4Vy8aZXxc11X12uIsjYBt13nz4S5qycO5e2Tt1DjX8rYSDJlzR3SpI0IEmSdvB1pyRJrZIkhfyrf7zljNmk2M3FZ6Ql1R9spLetkdALpRgovXhiXTTvn7zNNY8X2GFkLV/4cSeTpKIGlB//3WcEAAAgAElEQVQmMX5FEEsifqC0Sb4MRyXJt6CVNmlZEvEDpu4alB8mkVTUQMLUmahHKNhhZE23Xs/7J2/zxLpoLDcdI/RCKb1tjfgZWMlXItb8f+ydeZgkRZ3+38g76+y7Z5iL+xIRZxBhEQSBBRTBg2tZUBEUBBeBUQFdUVdZkFWRZQVBFFAUEFABRbnlEuQSF2T4DQwzzN1nnVl5Z/z+iMysrOqs6qzu6lkO43nqyczIs6oiPu8b34jMfJH+7Nl1dNdz7qSDH/kePfa6v9BNxSq9OLU9PQ1L6HV9O9O/ri/QfS95kOYOvIDufdH99KnXJ8O3R31b3T7s/Bv+2A/oTmf9lv7k6depvZH1gSwXtqEPLdub/uCxVXS7z91OpaWn0jNv+xutaDU6dvm59DQsobcOsZf9LLvwjzSz33J6wGUP0xWbS1S/51p6GpaETv3wqx6n/UdcTMU92Hsf3FVP0zPIEnqBtC198uAD6Ffvfoku+uTP2DDIe1+mRmmCrruQjaa5e8nuVLv5P8OXvSt7nUHXTVTCN8pd3bMTXXfhqXT/7z5E8wexRyUEQwSfPPgA+k1lO/r8MYdRd9XTdP6xV1Jxj5PZYzg2s9dLBi9d1++5lm5/xq+pvOdpNLPfclqoaHTyyvPorUO70psHd6Fjl59LK1qNZvZbTqWlp7IXqT90I31o2d700vQO7EapjSvpwJGXUnGPk9lby9Y8T1844YP02+r27LEGT/6GPvX6JJWWnkpzB15A973kQbrhW6fT6/p2pnfN341Wbvwm3VSsUnXvf6PKXmfQnc++g9Z+8/3Gsje5qaHsOX9/KCx7zx11SFj2xD1ODsvea8tPbFv2Sj/9Wlj2Nn/n82HZk5aeSrf+zK304gdX0kf2/qeGsveL5xrL3kuf+nBY9szHbgnLXvb9X6Z7X3Q/fWINe+l8u7J33657hmXPHFsXlr2tjr86LHtfV7ZrW/auzO8Ulr0145Ww7O1+3u8Tl70LpG3DsnfL3zbQ3b7EWh5H/fiJtmVvr2/eS8cuP7drYlCs1hJ93mxiQHzot02EkIsppRdMu+EWTpkFO9LCs38Ap5fw8z3/FSXbg+56AACV55ARCE5+5kZ4ah5eqhfvvfRpGDUblm5jw7P3gRMk8KKEb198BgZTEg5+4L9gTJShT5Rw2Y+fC8/zmSN2QGpAhdyTwZozL0dBtzFes/Gl866EY+rwHAvz3nUgZFXGU5ccCoVa4GoF/G73D6HqeKg6FBsNGwrHQeUJ/u3B74LvHYKn5nHAjZth1CwYmo2VD/wahONx7DmnYTCrYH6PgjM23gx9rABjooxLv30/XErhUuCEvbaC0qsg1a+C+95NKBrsmj517jVwLQNStheikoGYzuPR7x6JjMghxVM8umx/mCULZdvFc0UDEkegcARfvn05uN4hfHb1NhgtG6hUTPzllz8Pf4P9T/k0+jMSBrMKvpt9Gl5pAub4JC4661ewPAqXUnx4m14ovQqWPfIgyqaHqu3ioHNuhWNUYetVeI4FXlYhKhncc/lJyEgcMiKHVz50GMyyCb1g4O4NZUgcwQWXHAmlPw+xfwAXiYdgtGJitGzgD1f9BNRzAQBLP34C1IyEfEbCbe8pwy2MwSuO4jv/8kPorocdMxLyIo+0KuCgvz0AR8qgarnY57w/wtIqcIwqqiNrwEsKeEnFLy/7DAZSEnIyD+1zR0MvGLjhodcBADwBvnjuflD6c1D787jlXadhpGxgtGzi2kuuBPVcUM/FLoceDSUl4rHTdwCnlwCtgKv2OQ26S6G5HnICh4z/Ofb521nZVHux59cegFGzYNU0jK14Epwo4RsXnYmhjIxeVcTSW74GY6IEfaKKH978Uvi/fP6Ed0AdyELuyeK5476F8ZqF0YqJb33tSni2haF37AtJVSGrIp75jwPBGSVwtQJ+9e5j/LLJPqy+cPjskz8G0j3wlDz+6cqXYWg2/t99t4NwPAjH47SvnImhnIyBjIxjn7sK+kQJxkQJ37v8z3B9lJx88DZ+2UyjeMGPMV6zcOK518IxNHiOhczw1hDTeYhKCk9eejgyEgfeKOO+dx6Mqumg6nhYWbWg8hwUjuDcP34LfO8gPv5kGsWKCUOz8dztvwQAEI7HEWeeiqGcgqGsjPP1P8CamIAxUcIlX/kdLI/iiMV5HPS3ByD3DD5LKd1zNsxZunQpffixxxNtm0un2p6PEHIYgMvBIi3XUkovaVovA/gZgGVgBvw4Sukaf90FAE4B4AI4i1J6T+ffpul6EooBAfBRsPcRUACPUkp/O9uTzzZx6SHa+/4vQBtbB8+xsNWyQ/Hf574f964YxQ/+ScVTx5+CRfvtgOu+9zDKfqE/akkeh9zxXyg9eh82f/xrOO57j2D7nQfwwM9uQ3b+dhhf+TR2OuTjGF+9GhcuPwKfUVfC3X4frDz1ONxw28tYpIpYWTXxr/stxpqXxvGxn5+Nexcfib0WZPGRK57Ae98xjJ9f/RvohRG4lo4F7/kgfrR8f9zz8iguXUbwxDGfxaL9t8f1//04dNdDyfbw0R36cPCdl6P6+D1YfdgXsXOviKOufx6P/fI2ZIe3RmHNi9jl0I9idNXr+I/lH8Qnyd/g7fQ+rDj5X3DDHSuxSBWxpmbjXz+wNdb97yg+fNN5GHv4UeALl+GjV/wZe75jGDf96FaYlQJcS8fifY7AT5bvj/tWjuGbu5h45KOnYfH+2+D6q5/GdmkJT07qOHrXQbyyoYKTfngCXnzf57H7UAof/vHTeOKmW5GZtzVKa1dgtw9+HCOvbcDFyw/FcfqfgV33xwufOAmL3r8rfvr1u7HRcGB5FIcOp/Gh27+OiUcehvGZS3D05Y/jnbsM4TfX3gZbr8IxqtjmfUfihi/ujwdeHccFWxehzX8nNi8/Cdf99K/YOiXiuaKB4/YYxitrSjjxmk/j6aWfxh7z0jjyR3/BU7+6FemhRahsXIXdjzwGI6tH8N1zD8I9L43gRwf147kTPolF798Z1/3n/Ri3HOguxRHzszj0jotQePgBlD7xbRzzg0ex686D+N1Pb4PrWLC1ErY74COYWPMqvnT2kTh3eBOsrd+DNZ87Ftf/4kVsnZLwYtnA8e9dgFUrJ3HcdZ/DIzsdjz23yuCoK/+C/XYbxjU//E1YNue/+2Bc8cUP4A8vjeC/35fB08edjMUH7ISfXvpQbNkcOfpr2C7l4LhfrWwomzse9DFMvL4GXz33Qzg99Qrc7ffBK589Htf/asXUsnnDv2Hzw09C+cqV+OgPn8Q+7xjG9Vf9Or5s7snjiaNPjS2bL2/W8KnLjsHrh38JO+U5fPTnL+DhG29Fbv52mHj1Oexy6NEYW92+bFoexYGDKXzkl1/C2KN/biibN1/zaxil8Yay+ceXR/Ht3Ww8+vHTsXi/bXDdVX+ZUjZP/O/j8fIBX8BuQyqOuKaxbO562McwtmYjLl5+KH73vxvx/1aO428Xf6grYvBIQjHIthEDQggPYCWAQwCsB/A0gH+hlL4U2eYMALtTSk8nhBwP4KOU0uMIIbsCuAnAXgC2AnA/gB0ppe4svhqEhNv9EMD2/gUAwOmEkEMopWfO5uTdSITnUb7/28BLD+OcfZdj6YJDcOiBB+Cdl2Yxmj4UfYVd8dIjR+Hs954FAPjAtWfjXTeYGFs9H7mX7sfLp8goPHILvvbob5AXOXx73QNYdMadqIyswS8fXY1j1fuw9uvfxc13v4pL1t6DjfJ87HLk1/D84R/GgefPx64/PhuP3HkJNqQl3L76WTy+roSrRtbg7P9Yjg/tOozqAR/AK7eY+ODCHO665U4cZ+wEcj+P3//vE7APORgvjusAgD/QHfH7fA4/OeJcKPlBrL7r67jhvJOxTrfx2ZPeiR8uW4inFRHnfvlKHHnHhegvvIob7liJU47bFYsPXoZ/3rA/rrv3j8h9YEd8ZMdl6C9P4Jz+3XD/tScis9uhuHvxrrC1Eoa2W4In9n4V52y/JySOAC9ch0+/50sorngBC886CbvuOohLXrwC3/3+o9hKESF8dDk++LHvoTaxEYed9im8tH8Ja/90G/700ijOe/i7WFGi2Psj5+NXn/kkjhJN7FIwcPfF92KbtIgPvfoM7nt1HMeddREu5/fCgZ8+DE/vvBeWE4Jt3jmIPS+6Huee8S3wkopff+UD+Nvue8OuWnhi2XwsOWhnfOFd5+LB3X+G9OAibLzlTFw8tCfGTAeHPbcC3y28gtdeHseml/6K4oOXgF/5OL6w7Ef40sEW5h2/L95z418xtupV7PjgArzy3QtQevQ+rNNtXHbXlyAu+2csOuseyFfrWLD9Ufjd7Rdh74tvRkbgMLnm91h87p9Q2bQKPYNpfPgDh+KwH52F8257GVunRBy99jlcuerrMCuT+NyF56D/N19BdVMV15z4Q5yw/lQ8sb6Cp27+OZ4CcMdNF4M78nD8fVMVu5b/CnLewZB4DunDL8Jtv/gVtl+QRf6ypXjPcBqL9l2Inx77HRz1qe9DzvZh5Yk8+PI6bP+5E/DNj+6EJQdJ+NC7T8Tzv7sLufnb4fQlFr669eko2R4uvvJ4PLn7F3H7qgkYhc34wRWHY+lz9+LsD30HPAEuP+FxjK6t4PpHHsdWuy3FM/88gtGHHsN3vvNFHHTaD3HAQe/D8LH/g3n7nI9ttxvAlcsFXPKfD2CeIuCAZ+7HQk3AvI9cgP3NF/GRpQvw7zedj2XPbMAuWRl7vPAklh11AVxLh2Yfiue+cRlWPX8+XEpx+sa/4U+rC/jR6d8EJ0g4447v4Ld77IMVFQsH3XsoPnfFTbjz+Y3QCyN4/NeXYLc+Ht8ZeDfIs0P47H7b4diRz+P35F1Q/z6MTYWfQFz1Zzy9+ynY9p93wL4H7Yu9H9sGa2/8AfKLd8WqL26Fc865C5ZHcdlvvoB9nqLwHAv//j+PYKvth/HP+yzB37pDHHCEdONAe4G96Os1ACCE3AzgKLBh/EE6CsA3/PnbAPyPb8yPAnAzpdQEsJoQ8qp/vCdmdUWJOhaAlxF50QwADsCK/+sYlzK0XfiGr/t23ZPudNZv6Sd/8Sx11jwfDjPrOeTC8M1Xtw7tSvf8xj1srHORvdLypufX0+GP/YBe/8xaqv/+SnpFbkd60BWPskcQ+28pe2zf99Elp9xMz//d39mzcJTtwpeSB8/6v3vJ7vQdy+9iLzrZuJIuF7ahr551HO07/Nv0rpc20/INX6fX9e1M/+niB6i09FSq19hQyeeOOoRudfzV9AePraLGQzfSS9M70Oovv8WeIz9Roa+ceUx4Z3MwFPHf5W3phm+dTnMHXkCfen2SjvzXWfSOee+ge3zlbjZE0H/k9opTjqIDR15Klb3YfRZX9+xE9XuupfKep9FCRaPPH3MY/fOB+4exWeupO+gvBnahhasvoKl9z6Y9h1xIV3/5E/SPO7w7HH5or3uR/nGHd9PVX/4E7TnkQpra92xauPoC+ouBXcIXoyz65M/onw/cnz5/zGG0UNHYy+f9N4Zpt/0XVfY6gw4ceSldccpR9OG99qHmxAb2PP4VrLN45L/Ook+9PklzB15AN3zrdPrv8rbUffUvVNzjZLrd526nD+6xF33lzGPo6xOV8N6HS9M7UOOhG6m09FS61fFX0+eOOoSeQZZQvcZeAv9PFz9Ar+vbmZZv+Dq966XNtO/wb9NXzzqOLhe2ofZG9lL4dyy/i969ZPfwbuDMfsvp6bc+T7+pbEedF+6n5//u73TJKTfTx/Z9Hz0NbCixvOdp9KArHqVX5Hak+u+vpNc/s5YOf+wH9Kbn17N38hbH6Pu//ye65zfuobcO7UpPwxL6yKpx2nPIhfSb97K3gQWPCdnprN/S+3bdk56GJXTVWJnufPYd9F9ueIpenNqeflnchl784Eq68MTr6H0r2XDYilaje33zXvq+Sx+k1/buRP+NYy+4CV4xeTa/Nf1Bdkd6+FWP03dd8Hu6yX+72l/XF+jWn7mVnvPbF+i/y9vSryvb0bN+/b9029NuC19Is7HA7vE44uo/U2vza/Rsfmt65ROraf8RF9Nfv7CRnkGW0Gt7d6Lvu/RB+t5v3ReOIPrjyyN0wQnX0kv/9Aq75tT29Njr/kLVvf8tHKr8wDvfQ3f8/G/op296jjqr2ZDQC/+4guYP+ip9fHW9ri678I/0gMsepmaBvc707ycdQQc/8r3wkR/Bg/zkPU+j42VWVx/ffz+6+ORfsCcQPH8P/aayHR2/4ov0kVXjXYnhv3vpUlrT9UQfAGvgv6LX/3w2wtCjwUJDwfJJAP6nibuxb38E8D8ATozk/wTA0Vuqz+B3AM6klL7uLy/xL/zDs1KiWaaly5bRxx9v32RL8PXgJdgowWHgJdgoye+d+FjdOk7Sa0qwTbLfO8lxpt8oyfWwY02/zZYsA0DCa0pwxm793omP1aVrApL95m7CPznJsfZa0jfrMNGypUvp4489mmhbNZ1pFyY6GsBhlNJT/eWTALyXUvr5yDYv+tus95dXAXgvWGvhSUrpjX7+TwD8gVJ624y/GJKHibIAVhBCngKrE3sBeIYQcicAUEqPnM1FzFXqBgS6AYBEYJvuGFvqOhKcZ7rDdAP23ROeLfH/JjhGFyCa7Hed/bV0SzyTgHy647iJ6k73RCVJIrQrB9sA9g74IC308+K2WU8IEQDkwTqSk+zbcUoqBhfO9kRvxDQbUMwWvLOF/6zPP83xZwumuT//7CA/W8DPFqhzff5k1zANiKf5k2YL8tmevxvX0HmiQHfE4GkAOxBCtgED+fFgrwmIpjsBfBKsL+BoAA9SSqlvwn9JCPk+WAfyDmBvh5xVSiQGlNKH/dDQDpTS+wkhKthL7SuzvYC5Su3KQLsCNFMBaAe/2YB/xudsd762v83Mzje7c3b//5j2nG2OPPPfp/31zPycM4dmu33bgXKuzjn9edudc+bi4k7z33SUuiAwlFKHEPJ5APeADS39KaX074SQ/wDr27gTrC/g534H8SSYYMDf7ldgnc0OWAh/ViOJgIRiQAj5DIDPAugDsB1Ys+RHAA6a7QXMReo2eGYCyLb7tF7Vcr9ug7/bIrMlYd9tyM8U8DM7VxtgbUGoz825Wh+zHchnCvH2v2W3WwR+ol1rGYBSejeAu5vyLozMGwCOabHvRQAu6sqF+ClpmOhMsH6Cv/gX8gohZKibF7IlUqvCsyVEoNNztDx+60vqGHadnmMmwO/8e7c5R4ujdf69u3mOzuHeEtBdhPpMgD4TmLf+Lq2P1fq7zOD7z6JFNdPUpT6DN1xKKgYmpdQi/vhavzNjjqR3dqmTStsJqDqBfzfA3wmQ5+q47Niz/d1abNsBdDuBejdg3i2Iz9WxW/0e3QJ2/HXP3bEBwGtx/LjjdPw/dLWVQAHP6eLx3jgpqRg8TAj5CgCVEHIIgDMA3DV3l9XdlBRocyECSY8Zx4hOHO9sjtmJq08K/Dgod/Z9ZnvMZCCO3W6WcJ8rsMdfa/z+SYHeCcw7AXlH1zVHx2137Bkniq6Fid5oKakYnA/2HIwXAJwGFue6dq4uaqYpKeiac5LAdabwnymkZwr87v4GMcdq2jOpk5/y/RKCPQmAkwKmu8eakpUItkkhOxsxiNsuCVgTf/eEkO4E/HHft9t53UkU8N7GYkAp9QghvwXwW0rp2BxfU9fSTCA4ExGYiQDMBP4zAf9MoD8T4M9EOLoF55kcJwnguwX3bopEM4gTnX+OxaH5+3UT4m9EQXhb9hn4z8H4OoDPgz2CAoQQF8AVlNL/mPvL626arRB0vH/z+acRgM737wz8nUK/0/2TXGOnkJ7t/mybxuVmSEwH5m7AvXn9TGCcBOozAfpMADxX2ySFujPD/bqS3o5iAOAcAPsCeA+ldDUAEEK2BXAVIeQcSullc32BSVM7CHUC8XYC0Am8OwF/O+h3AvxOYN8J6DsBfCdw71wYoudMDvTpYN4JyDuF75TvMIv9OwXpll5uBnSiY3QoTPHQ99pu01VRoBTwZj2k/w2ZphODkwAcQikdDzIopa8RQk4EcC+AN4wYRFO7IWWN8GwNm/bQTQryNudquU9r+LcDeHt4x+/Tbr+ZAj8p7GcK+nbwnOl+QCOk255jhvs179sJ9OZq27hlp5NjzRLknUJ8utbAnIpAJL0tw0QAxKgQBIlSOkYIEefomrqaWsG0VWtgJiKQRACSuv/W1xvJbwnwNudIAP2k8G61T3uRiOR78cdi+7S6ts73aefCZwL4mcI9Kay7tW6mQO/s/MlBPhuIT7ctbTYAzevbGMOZJfq2DRNZM1y3RVPnYKVT86bZFqgDvVP4x11fN6A/G9gH+Z1CvlPAx8G0k22BZHB2Z7H9TKDbrXmgNbwb5mckPK2BnRTWMxWkKKTbAXpamEe+wpTWdZtzTjluN0XhbSoG7yKElGPyCQBlDq6na2m2QjATEYgTgOnEaTqgxwnFdOCfDuSdQH864Hfi5DuB/UxBnwTy0823gl6SY8wG7EmgPmtRSQj6VjBvBnLL7Zp4SVv8h83HaAfx5nXthGTqdaI7idK3pxhQSvktdSFzlZIKQaciMP12NPb40e1mA/924J/O7SeFfpxrjwP+dNslhX0nopAU0DMVgekAP1O4JwF7t6CeBOizhXkrkE/p+2oH/JbXhtbbTScMbURkNong7dtn8KZK7QHcCL1WIhDv7uO3aef+m51/APR24Z74bRqh3wz8drCPC+k0A7xdGKfdNkHFbOfO28G9HdiTgrpdXhxcY7fvSGC8luunA/h08O4E3K2gPRtgt9wmAaiTnCvp+dqds921sXVu2+XuJAq4b8/RRG+J1KkQdCoCSQWg1fq4Y04H/9br4a+PEYsp54iHfjPwY/dtIQpxTr4VqJPAvN027Rx7crGZHvAzEadYcCcEeydQnynQO4V50nBNkhbI1HO6sfNT1rntYb9FxIDi7RkmejOl1mBOLgTdFIHpBGA28J8O/DOFfqfAb4Z9dH620ySg7wTyiY7bZl0c8OpgjVkX49jjoBsH3OmgPp1Dn5uWQCuB6hzkbbfrEPBem31b5c02/SNM1GEihCwC8DMAw2DcvIZSejkhpA/ALQC2Bnth9LGU0oJ/t/PlAD4IoAbgU5TS56Y7T1MrEkC8EHQqAnECMFP4x4G/HfTbuf12wE8C+05A3wnAk8K9U7BPB/Wk19jOqTeva+fQmyEeB9j25wqz2sK73XGn+x5T1rcAZhJYJ5lvXvY6gX0X3D6NeVbQ3ISIALyNh5bOJjkAllNKnyOEZAE8Swi5D8CnADxAKb2EEHI+2EPwzgNwONjr23YAe+nzVf40cYrRhYZ+g/awbyUQNDx2FPRRhz+dAMS5/ukcf5ybj3P6ceCPA3sc9NuBeCbgd+LWtT3H3IA+KeSnA3w7CDfDvZ3rTwr26cNIU6HaLaC3gvlchm2aIZ7U2TeLTavrabX/rNM/xKCzRCndBGCTP18hhKwAsADAUQAO8De7AcCfwMTgKAA/o6xGPEkI6SGEzPePkzg1AD+Yb84Pr7FdfjIRSCIAUfc/U/jH5TeDP87tT+f0p4N8UuC3c/btYN8J6OcK8tMBPklIp5OWB8uLgXkMaKeDdzeB3ln4pnOQdwPi08F97loEwQko3q6Po+hKIoRsDeDdYG9KG44AfjNYGAlgQrEustt6P69BDAghnwV7BScWLVoEoNF5AU0OC/Guv1kEmsNBzQLQ2IJoFoWpkG8V9mkG/3TQbwZ7FPitXH4zzJPAPxbQtNX28YCfTlji4BsH9nZQnwnQZyoKseuaIJwU4NPltYJ3uG0bF956vj2wk8A6KajbQTgJoOcc4l1LFNSx5/wsrcLpTdvsARZByQFwAVxEKb3FX3c9gPcDKPmbf4pS+ny7c865GBBCMgBuB3A2pbQcvC0NACillBBCW+4ckyil1wC4BgCWLlsW7tt8EI82unqgvRA0twTaiUBzCyDO/ccJQDPoZwL/TsHfvNwO+q1COEmAH3XyncA+DtRxkE7i2pMLRTLAx7nmtmLgz3cC9m5DPQnQO3Xd/+dufBaJcF2+VYpiS7UMzkd8OD2aagA+QdlriLcCC8XfQykt+uu/RCm9LekJ51QM/OcX3Q7gF5TSX/vZI0H4hxAyH8Con78BwKLI7gv9vMQpaAE0C0Ej9KeKQKuWQLMANLv9oAXQuAx/G9rg/KPL7FppLOCb81oBvlV+M+ybhSMO9FP2aVpuFaphMI/8xhEIt4J7o1AgPG7cflPy2opMe6BPB/N2IJ+Z+28N8LadrQnAPZNRM28kYLcDdKt1cfnNeYRvXOYi6wnHd+X5ORQ0trU0B6lVOL1+LZSujMxvJISMAhgEUMQM0lyOJiIAfgJgBaX0+5FVdwL4JIBL/OkdkfzPE0JuBus4LnXaXxCXkgrBdCIw1e1PFYF2AhCFPxDZrwX82wlAnFsPwB/n8DuBfjNwmx19M7hbQTvq5JO0AOLcexzoZwv5JIBP4tATxernAOz/11DvFOSdQpxrXtcEdDblplkfyePreZWWV95BogCSv+lsgBDyTGT5Gj+ykSS1CqfHJkLIXgAkAKsi2RcRQi4E8ACA8ymlZrtjzGXLYF+wR2C/QAgJYlVfAROBXxFCTgHwOoBj/XV3gw0rfRWs+XNy0hPRYNrUKuiGENg+vW3Pm5UAtII/0B74za6/GfyN+3ux2zafoxX0m11+nMNvBn5zbD5ZiyAaJpoK4iSgnw7ySUI8bN6LyUvm3JMNe3zjgX1W7jshyOMgzea5luvDKc/H5nPN27WZxh1jyuOXZ5RoJ2GicUrpnq1WEkLuBzAvZtVXG844TTjdj7D8HMAnKQ2HOl0AJiISWFj9PABtX0g2l6OJHgN7lEdcOihmewrgzM5OUnfrXtNP1UoIXC9eBFwvviVge15bAbBdLzH8LdebMfSnA34c7KeCdyro46DdCvLNrYDWrQgXrVLfzw4AACAASURBVOA+G7C3FofkQE8Sr2+X1y5/LlJScLdbng7Y7Rx3ElC3g3QrQDfuT0A4hgpCCLhg3s/nSGQ9R/zt2bbsO5Ep2wbbcxwBIWz712N/yQ4T7V4HMqX04FbrCCGtwunN2+UA/B7AVymlT0aOHbQqTELIdQC+ON31vGXuQAamdhpPJwQB3JtbAtH+ANttFAE7EIKIIDiuFysAttca/pbTAuox8LccLzH446APoAH8zdCPC/3EtwKmAj8K7NhwTgvYdwP0M4H8/yXgZ+S8p4F6bGikyX3HAT3OdbeEeIv9ApCzZRKCvAHcETgHEAfQsG3sdj7Aw20i2/MxH6FhmWtcT9j0sdZ/TQeJbpGygtbh9DARQiQAvwEbkn9b07pASAiAjwB4cboTvmXEoGE4oA//VkLg0npIqD7PWgPRcBCbnyoCwXwU+u1aAFHwBx8zyKPNIuEmdv2u47V1+3Edtu22m9o5PNXhtwN+nLPvFPbtQD8d2JOCv1upU7jHgT2JU08aBuGaQN0O6NG8OOCGzjoG5t0AuSRwLSEuCVwI8Og2wT7NxwiAL/rn5wm7JpFnU56w7/Hfnfy5rRLFlhpNFBtOJ4TsCeB0Sumpft7+APoJIZ/y9wuGkP6CEDIIFp15HsDp053wLSEGYUvAnw/CQ81CEISCXC/aMqi7/6BVYHt15x/MBwIQbTkE81H4R8EfzDvBPI3me1OFIfpxvClu33W9tiBPAvtm0MeFcZpB3+zo4wCfFO6dDquMW+5WSgzuNhBPAvBWzjrOjbcDdytodwpsnudiYS00wZbnCOQwj6vDlzAQx0G6YZ7nQig3A1r0t4nOixxp2L4OcwJCAI7AFwlGOJ4j4MC+K8/BPwfAEz/PFwFCANK1MkQ76UCe+VkonUB8OP0ZAKf68zcCuLHF/h/o9JxvCTEAGoeRBq4/FAU0CkHQGqjD32toCdh+2Md2vSkiEK7zYR4Av1kEogJQX9cY8mloMTTB33Onun3P8dqC33P8IaNuAOR48LeDfrLQz1TgT+fktyTsO4V8EsBP57KD/Vu58pmCnePj1/MC1+DAOZ4Lt2t20AHQJYFvAHXjukagB+68Ac7+NYkcgRiAvg3IGax9eAcg9wHNEwbzID+AOM+REPSEUhDPYU6cev68A3j+vEvredQDcR2ARvI8l8X3PRfU7tKNYhRbamjpFk9vejGgqIeIAgEA6qIQxPujLYJACIKQEJuvtwCCTmE2XxcB2w/rNAtAMA0cvuV6sQIQ3b4OfS8W/kEroB4Sau/4WcgoHvpAo+tvBf3GbZM5/PgROnMD+25AvhPAx4VdOoE7c+Dtwc4JXBha4YX6MfgmSE8HdKlp+3DKcxGIc1OceR3sCNeLHMecdhhymerIg2UuXK7nB06cBJAO4Ow67ImfrjsV4p7L1nlOBOAW4Nigjs3KkGOzPM+F5+eH23keqGPBsx1Q1wunrm2Hy91JHY0melOlN70YBCkQBerH+N2IELi03iJwPRoKQSACzS0Bx59GWwGW4zXA3HK8WPhbjlvPD8JEjheC33VoGOP3HG+K449Cv1kQqEfhOk5bl98M+/gQkNeQB3QO+W4AflqYx0C9EeBcZL4O6Whes1uPbscJ0hSQ8wI31YH7YZdgXQD5aLiF47lYgLN5Nm0FbymyHAVyFNyiD/XoumZoB6GSKJijeZyfLzS57jqszcY815nitqljA44FatugjuUv26zc+MvsY8Gz7RDcnuXAsx24lg3qefAsB24E3J7lwPM8eJYdAbk/tVxW9i0X1GVTL7JMXQrXduG51N/eY+dwKTwryG85MrOz1MXRRG+09JYRAwBhpzGbJheCxml0fV0EomJgBsuhCDSJg+PFCoDrtwgCyLMppuS3cvuebU0L/jiHHxv/n6ZjN0izgX47wAfznYJ+OshzojTFrUcdeghygbQEfODQg7worBnI+Vi4N4NdCgHOpkLTcjPYRZ6BmiNomMaD3g+l+OAmrgVQD3AsBm6nDvnQeXsO4PqwjrpuH+5wrLrjjgDfs506sF03XHYtpy3IGYg9uLYXQtm160CPQrx5fQD18GZMvy5bEaPHpmhYH50CviFE/em5s0//aBm8QZMfAqJT+wxYS6HeURwVAsNhfQOG48W2BuyI29ctNwR8swgErYCoULiON0UAQvg7XhgOCloFQX4z/OPATz0XrmM1gB+Iun6vQQy8Fp290WnzfKepraOPOPs42HcC+ijkCceHMfMpMI+BfH3buoOPOvNmwNfzmj4xcFcEFjtXhDbO3Yd5HNjDzlXQENYNELddBvomqFPbbHTolsFCJaY+LdBd2w6deejYbSeEuWs5vnt34dpugyN3bVa+PctrCfooyC2vFcxbQzx8NAum5kUhH7cczZuTtOVGE23x9CYXA5aCEBHrJI4OE60P3WTgpzDdugAYjheKgOF4YUtAt9yGUJDleNBtF5bjNoqC4zXAP+r4PV8EgrBPkNfQT+CDPQp+z7Fauv1m4Eddfrdh3xLsMYBvjsM3Azy6XRCaCcI0vCDEhF4YzDl+KtSDPJ7nIERAHQBdlXhIfCPMVYlvgLks8D6s/XnOh7kPcNnvPBV4hB2nfCTEInKRcEvozG3AtRiwA6C7Dojpzzsmc+GmEcI7cOPUMkAdm5UDy2gAt2OYU1y4azvwbBuOboF6Hhzdhmt7IYQDgDcDe4oLDwY4+ICOgnuqA4+EXjHVfbeaD9JMAM2T6DwJ83h/hFEwH83ngIZ10SnKnV9Dc6KgiHuZzlshvSXEAIh2HPtP/ES9/8B2o+Efzwc/C/fYng/3SEtAt9y2ImBarg96r16p2ghAvWXgNMA/EIM4x98K/u3u5m2eT5LaQX86Rx9167wgNQI/EpMP4B1AnRNYKCaY5wiBIPFtQa9KQgj0VAB3H/SqyIeOXRa4KZBneRwkIQA7gcAh7ERtBjznslg3cS0WdnEbYU9cOx7upsFcuWUwyPvLnuXANSy4ts2mPtgdw2pw465hhyCfDuqu5YWO3fa8BphH3XezM48D+lzAPAB5AGuJqy9PB3GJa4S9xAXTpvUCB17iQXgCXuTBS2yZF3lwEsdagSLL4yQeuHVlzJV2mP7RMnjjpvB5RGGhZh/HRRgSMp2oADDQG25dBAIBsBwPNcv1BcCDbjlTBMCxmsHvwbEZ7B3bDUXAdTzm8pvA78a0BlpBP25YZ7CcJE2Jz7cAfRLIc4JUd/qCFEKb86fNrp0TOAgiFy7LPsCjYFdFPgS7KjW6esWfRqGeEvnQvQs8g7ngj4gJgC5wBAIBiGvVAe5E5k0bxLVAbRPU1AHHhueDnFrs4/r5rmn68GYQdwyrAeIM8E7ozD3LhW04IcCbwe7581F41wHeKiZOYcfAOg7anQC7GdZRxy1xjdAOQB3AeCqY2VTyO8AJTxiUJc4HMx9CWVCFENyCIjBQiwIEVQInihAUKVzmFQm8KLKpIoEIEoisgEj+J5iP5FNeAgQRlJf8jwgIbB639ib/gVolSll/y1swvenFAGgUhKgw1FsENFYIdNsNhaDmiwHLYyKgW+4UEXBst6EF0Ljs1UM9ttVSAFh+Mvh3Cv648e0A66BlTn0q9KPAD/KDEE44SiYG8IGbF0Tm/lUpCncBqsj78OfDjyLwoVuX/Zi7LHB12LcAveSHdTjqgrg2iGOAOBZgMbgTxwJxzBDyAdipaTDYR+Zdw4KjmyHMA9C7humHZiy4lgvHcOBanj91G/Ka4V4HOxqmzc7cQ6NbB2YH9ijU49x3MBXJ9CCPOu4otAO3zfKaoK6IDSDnFQm8JIBXJAiKzJZluQniaiPQ5RQDtyACPsCpIIEKCsCL8HgRjt/CtzwKx6/TjgdY/g2jNduv25YHU6+bQNNxYbgadKsLMSIAW+qms/+L9KYXg2jHcf0x0PWHzAWhoagQ1HznXzWcUAgCEahFWgmO7U4RAcf2GsJCju3CdZwprQDX8cXAjYaD4gVgtvBvGC7Z5PajgA/Az/mfIJQjSOIUly+IAfz5UAAEkQ+Br0pC6OSzilAHvi8ACs+cfwD7lD8vchwknoVpWAwfIewFAhDHrIdnLNOHvu/mzVro4D1dq0Ne10AdC07NCCHP4F6HfBCGsQ0ndPCO7sQC3jZduJRCd+thF8uLC8PUXXtcR2cnYI8Lq7SDegDuOKBLXD2Ewtx3dL6eJygCBDWAuBzCPJyqDOZcAG0lHQKcU9J1Ny4odZALcghyyovwCA/L9WD74HY8CsuHueXWoV0zXDbv10/btaFZeoNZq5s3JzRvluWyempH6mOLFnzX0j/CRG/MRBH0EfgV0G9uG35oqGYzIajZrJAZjhuKQMVwpoiAbjgM/C6FEylobgj+QBjq7j8O/p5/o8xswJ8E+JwgNrn5OuzreQHU+RD0gsiDEzhIMssXBA4ZRQhBn5GDeR4ZRYAi8EiJPFIiC92kxDrgJZ4BPviIgZMP4O4YILZWd/S6CWrpDOI+2KlRn3dNE45mwDFM2JrRAHXHsODoNmzNhhuBerOLt2w3dOZxMLe8ety8OTQDNM63S1GIx3VaiqQO7Thwq8GoI4m58UZg8z6wBYiKAF4RIaaV0HGLaSWEtpBiYObUNIN2AGw1XQe2KIfTANwuJ7L+MpdB2vQoKj6wjbDOsKluu6GZ0suRemQ60C0TuqWF9cixfVENTJXtNrSkm02Ua+lhKzpqqoJ61FyHgvqTxDxFQ6SzTnSLPahui6c3vRgEiTaFh0zfcdhupLnoC0HVYPCvmg50y2GiEIDf8mCbTtgacKxoYWb9AK6pN4pAJCTUqvAmKbjt4B+EeKKxe16QwEtqmCdIMjg/fMPCOHUREEQeosxD8gHP3DyDfuDsM4rgA599ZJ5DVubD8I0s1IEvBW4eHgO+Vas7edsEsXUf8jXm4n3YU12DU9Nha7rv5C3YNQOuD35Ht+H4gI+C3gmdPGvdNYK+8dMcimk1PLFVinZcAo0dms1wZ58mZy7yU0AegF5QBUjpekhFTKsQ0gqEyDwnKSBqGpyS8qc+1OUUc96iEpkq8EQlhDn7+L9PBOg1w0WtEpgiG4ZjTKkLVcMOYW6bDhzbazBEThAytV04lh1viGLqguvH2LtRF3jf4ETrQpwJitYFZoII/vbM1dNQJFn6x2iiN2CiQOQ+A1b5qX/HcNhP4DsZzW9iVg0nbBFUDRsVw4FpubCCwm/7FaFJBFxLDwu6Y+lN/QJefUjoDASgFfzjXD8vqeBlJgCCyIfgF2U+LPiCyEGSBcg+9DOK2AD9fEpsgH5GYg4/IwmQeAKZJ6HbVwQCMQC+Y4BYegh8mBpoAHqtDNeogeoaXL0GRzNgazpszZgCe1tj4Rq7ajcCX3dgex50tw72xlBNY1gm6uiB9pBvjq03d4iKMYBXeQ4SR6DwfgzdD61IaSmEu5QRISgCc+xp1Ye6AiGlMLinVAZyJQ0una2DXcmETt0TVcAHvE0EGC6FGYG74Xoh1Cs1N3TpNdtA1ayGZToAemBuokC3TAeeS1k5928GcyyzpbEJQe63djtp1TYPVuBEKWzNcoIYC/GwTEsigzfPQVT4BjMjSBxkWQhbsBlFRNZfzigCcrIAWWDlOSjbWZkPy/IOFydnS8tE2X0Ub8X0phYDwL+/IACEBxZ/tFmFqZoOaraLsumELqio22GFqRkOLNOBbdabtXUn5IaVxbX0MAwUrSxxFSVJJQlH6oSjc8SIo2GVIoA+LwgQZSGsDKIsQJIFCCKbz6dEpCQeeVXywS+EsM9IArISH86rIgdFIH7HLYO+SB0QWwexKmyqmyCWBk+rwKuV4WkVUEODq5Xh1AxYZY1BXTNgVTQ4hgVbs2BVbViaBVuzG1y9ZbvQXQrd9WB58KeNDj7OxbdKzaEY5sq5EOgq3xiCYVMmlqIPcVERIGZESGkRUlqCmJYhpFWIaQViWoGUTUNIK+DTGRZ2SeUYyNU0iJoFlVR4YgpUUkHFFDxR8fujKEyHQvPDLprlomrVy2LFcqHVHJTGbRRrtg/vAirGGHSDlUMGcBeWGZRB35BYeghuJ2JMmqEduHKgNbCbXXcA68CAcIIEQc00tDx5WQ1BLfrlT5IFCBJrcQaQ7klJUEUePSkR+RQzIRlJQEpkZiNoaSoCB5knDVPeNf2yqIPYNXCWDmpUmeGoVVhLs1aGq1VY+dushWbD0XRYZd03GRYsjbUwx6t2GDrsRqKUdvE5R2+s9KYXA6BxWGn4KAmXhRMMf+ho1XBQ8Z1TsWY3VL6gVRBURNswwjhm0Apwgzs7m+KanQgAJ0qRkTtixA2prNKJEkRFaQzryEwMRJmHqgh+JZOQ9YUgCv68zMI8eUWALDBHqwrscQkyB3CWxsI5ug5iaiyUUynCq1XgamV4WhmeUYNdrsGqaLDKNdiaDqtSiwW+5U8N14Puej70G118J7AP3LvK18MyEtfo2plbZ65d5QmUSAhGSktTIC/m0pCyKQb5XBpihjlzLtMDLs0gT9QsPDnN4ulyGlRKw+VE6I4Hw2GtS91mI1YqloNqJYC7jbIxjlKNmYuSbqNi2CjVbL9cuZEyFbQ+62APTEbdndfDKkkMRpy5EGQ1BDozExFjIakQRL4B4sxc8KGxyCoi8qqInpTI3LYiIivxPtR5ZCVWxlSxbirUKMhNDZxdA7Eq8KpFFiacLMAzaqBaGXZVg1X2y1bNgKbpKJR1WBoDuO2XMcdwWHlrYybsSOswKGPA3N+B/I+WwRs0BXccU/9JpMGIhKDPIGgRVEwHpZrVIASmwSptc0V19GrowAIRCMJASUQgTgDiKqigZiBIcui2ovCXVQHZlIisIiCvSqHT6lXrbisvCyH4VYGDKrDKKQVu39TA1aoglg6vWoRXLcLVKqC1MpxyCY5mwChWYJdrsGs6rHINlmbDLJthpQwqaBzwdddrqJBB6AaIr5BxNxFFYa/ywYeBXhJ5SBkRYlryAS9CzEiQczKkbAqSD3opl4aYTTG4B5DP9oAoGQZ5KQ0qpeBJaRguheFS6LYH3WHTkmmjVGShw5JpoKRXUKrZKOp2WGZqug3bdP2WZHyZaTYOMykzwSivVlBvVWYklbUYsykRPSkJGVnovMxYGoilg7OqIGaRmYNqEV6pXmasssbMQbkGo6ajXK7BLJsNIJ9JmWlnEPimYbFBmQnKSbTMBOYgKDNyTg7L0NU/6cqLL/8hBm/UFNxx7HqA6bphmKhiuSjpzKVNaBYmqiZKNRumbsPSWWsgmDq2C0evwrV02Ea1waklaQVMqciSGjr/wPXzsgpRSfkVmMFeVkWIMo9s2oe9KqE/IyGfEtGjiuj1Y6K9ioi0xCpuSmRuX/QsEKPC3L6mAVoBbqUIr1KEWynA1aowixWYhSqsSg1WWYNR1GFXLZhliwHfcGBWLOiuh6rTWGkNf+x8812szSlaWSWOICNwsRU1I3CQUmJYQcUMmwZgl3uykHszLESTyzGg5/vBZ3tAUnl4chqenAWVM7B5mYHc8TBpe9AsH+iGg7LpoGjYmFzHID6paSjWiqjWbFiGA9OHOps6cCwz/O+bxd9tumGw1f8eJ/pSNgNBUiEoGfbfyxITeoWHrIiQ/P8/nxLRn5HRo4roy0joTUnISjxyihiCm/3n7H9PCYT952YVnFkBMTX2n5dGQWtleJUirFIF5voKE/eKBrOghcA2yyZGyiZr0RlOw/8euG6j4Wa49uIeHeIqcQRKpAWXEdi0T62LupyTIPlTpTcNKZuGmEtB6clCzKXA5/vBZXrAZ3vAZXtA1RwLySlZeFIaJuVQsz3oDmutlUwbFdNFwbAxajgoGzYmqxYmNCsUckO3YeoOYt4c2XGilML7x/sM3ngp6ECOjiIKxi1rlhN2rJVqFqoGc3SW7rAWgeG3DAxjCgxcy5hWBFqBoA5+BgEplQ7j/JIqQEmJkFUR/RkJfREI9Kcl5GUBvaqIvCwiI3NICxxUkQkAb1YZ/KtlQC/DqxTglSZgV4twyiUYEyWYxSqssgazWIVZNkPom2UTVtVm4TKnDv4o9OOGVwapMT4fVHquwZFl/JvLAifGQM9cvNyTCWEv92Qh9PQxB5/tAZ/vZxVezsJTcvCkFKvstgfN8VA2XJRMB6VJGwXdRkEfDyv7RNVEsWrB9P9TS3dCyNtGDa5ZF3fX0qeIu+dMvZO0+X8VZBW8pITCLigZ352LIdQlWYCSFpH2Rb0vLaMnJWIwJ6NHEZGTBfSpzKEHgp6ROMjEA2dWwBkM6qiNwS1NwBsrsv+2XIZZqMAsVmFUNJQKGvSCAUuLCHrVhua4U6BuN90n0b6l1ui4ByQm3sH/qigCE/C0CDnPBFzpUSHl0pB7MlD78yHIA5hzuT54ShZUSsNTcnAFBVXLhea3yMqmg3W6zf5bw8F41cRE1cJk2cLERtMXbxOGtgGW6cLSbWbc9Coco9pQVwPhjj7eJe5/7Vb6x2iiN2gKOpCjISLDv3uYxXEtVAwndAemwaa26cCqaWHzPggNuZbRtmBFWwHBMLcAFqIPClFJQVJF5v4VEUpahOILwGBW8acyen3336eKyCk8VIFDRuSY+zMr4PQSSKnCXH9pAm5pAlZpIgS/Wayy+bIJvWDU4e+7/ZLthXAwvLrzmw76QcerwjXCPgp8OSdBzstQexUoPXVnr/TnIeZ8V5/vB5fvB9I9IextQUXF9qDZHmo2xaRuo1R1MDFqYaJWwGR1BKMVE6WahfGyCcNvzZm6EwLB1kpTgNA8rLE5RR+pIaoZcILoQ70OeFGWIPv/m6SKUFIi8hkJ/RkZQ1k5FO1eRUReETCQkpDxW2wZiYPkmsyxGyVwZgVe8TUG9/IE7EIBxkQJVqUGfaKEQkGDWbZgFIxQsA3DCf+zquOFYj0d1JvvX1A49l8tUiNC7btxOcf+MzknQ+5NQ+nLh/9b6Mrz/eDyA/CULAO6nIVOeWg2Rc32oNku1tVslAwm0COVOshH1xuo6TYMrQxTn4iYL2a6ouKctK6Fwiw1CrOSG4SUzvotbTE0W6k0a2H3pWXfdEnoUyV8Ytm1s+bNlhpNRAjpA3ALgK0BrAFwLKW0ELOdC+AFf3EtpfRIP38bADcD6AfwLICTKKVtn6PxphcD9o5jP0TkDyMtGzYrnJqJ0bKJmmZB9x2koVmwdD0smKxVYDSM0GgnAFGnKCoZCEoGUjobwkNWRagZCX05Gf0ZGfPzCgZzMvoUEUMZGQMpEWmRR1Zm4BcdHZxeBKcVGPQLzB2ak2PQRwswi1XURgswijqMggG9YMAoGLBqNkq2G8JDaxp73wz7qAtU+TroMwKHvMimcpbBIjWghpBX+vNQh3qYo+8bBN87CL53CEj3wkv1wlNyMDgZVctD0XZRNlxM6jYmahbGahbGVpsYrZjYVNyEivY6DM2GUav/FwHYbb91Nh0ggjuqhaDzXUlDzvaBl1SI6TyUtALZ/y8kVUA2K2Mop2AoK2MwJ2Mow0R4KC2hxw/B5CQOKnFB9BI4owRSnYRXmoBbGIVXmoA+Ngl9RRFmsQJ9vBL+DyNFA2tLFjSH/Q+BOzciw2NbiW60Q1zh6mKbFzkMZwUoPrDVXgXqABPb1FAPE9ueHvC9Q+B7hxi01Ty8VC9cOYOK6aJqe6haHsZrFgq6jdGajZGygdGyibGKEYqsoVkwSjbMETs0RraxEY6+clqBjYprEBZlRogJq5RKQ0lJUNIieoczkFUBAzkZ8/Iq+jMShnMKBvz+jMG0hIzIISPxyEgceL0IzqiAqxXgFVl9cAujsItF1EZHYEyU/f+iBn0tE1OjYEDTWdiL1Ym6AVrjUazqUqfyFhxNdD6AByillxBCzveXz4vZTqeU7hGT/x0Al1FKbyaE/AjAKQCuanfCN7cY0OCtZvWWQc32bywzHRRrNswgNBQ4S12HrZUahcDvI2iGT5wIBAVeTOchqSqUtIRUhhX6nA+e+XkF83oUDKQkDKQkDKWZg8zLPNI8Zc6xUgCpTsItjMItjMIYH4ExUYI+WoQ+UYI+oaE2XheAqlkv6FXHC+O67cAfxGybYZPyIaP0Kkj1q1AHslD68kgN9ULuy4Pvnwe+fz64/ADcFIO+LagoWh7KFgP+iGZhdMzEhDaOTUUDm0s6Rssm9KrFIOND39IqsGulBtg7ph4bpokCRlb7IChpiCoTXAZ6CUpKhJKWkM4x1zcvr2J+j4KBtIShjBxCPuuDRfFMcLUCOL0EWtoAd2Iz3DWjsCYmmchOlLB+tITaRP23NivWFKGNjmJphns0zKJwBL0ij0Wq/3v7IRa1V0FqQIXSq0Ad6kVq0Af74HBdYLP9cNVeUDWPqsej6v/eGzUf6pqFzSWDietrOooVE3qlAKM2CkOzYOombK0Eq1aCa+pwDC0crdRJ2U71LwjLtpJmwprNypjfo2IoK2Nej4LhjOyXbxF5mY3tz4oEnM5+a06bhDOxGV5hHZzCGCvbrxZDY1Mbr0Ef12GWTbxUs/3wJWsRNRubaIr2U6g8QZrnoPKsXOdFDvN6FGzt/9ZqrwK1n7V6lP4crvjC97uCHW/LdCAfBeAAf/4GAH9CvBhMSYQQAuADAE6I7P8NvKXFAEG/Qb2vIAwP1SxUa3YknmzHVpZWYAruZoxWFCmVD92nmpFCKM3vUTCYVbCwT8VgWsb8LINSNhAAzmVAmpgELY3CndgMa2Iz9JEx6GNF6KMFaKMV6AUD+rgOvWCg5IcMgsrRCkbNLjMdcfs5kQ8BlBpQkR7KQunPIzWvD+qw7/IHF4Dkh+CleuCl+6FTHiXTQ8l0MV6zsHmTic3VAjYXN2FTScemogG9aqFWMf3Wlg2rMgmrVpoirq1gH/+bpqBmJSgpKWxZLexNYWGfGkJ+flZGRuKRl3lkBSaqvDYBlNbCLYzBWbce5ug49IkSapsnMDlRRW2cgccoGCjX7PA3bYZOnJgG4bI0z2FAEn3g8Ej1qSHcU0Mswwd7GAAAIABJREFUZq4O9SA1rx987xCEwQXgegbhpvvhpfthcDIqpouS6WG0ZjEh1UxsLhpYX9Cxaa0OrWJBr25ArbqaldVKEY5RhaWVpoV6HNDV3nmQ05nQrKhZCT0+0Bf2qhjOslbS/IyMnMJ+07zMM1euTTCjMrEJ7sRm2BPj0DZPQP9/BRgTFVRH60bl1SoTzqpDUbLd0KTo/itjoyl6/4fC1VukeZHD4uE05JyM9FCagdwXzNT8fgi9g+D754Pvn8dao+l+WGI6LKdFw8amionNvlhuKhlYP1mDplnQyiYzJmMWgC6IQWdDSwcIIc9Elq+hlF6TcN9hSukmf34zgOEW2yn+ORwAl1BKfwsWGipSSoMmzHoAC6Y74ZteDFwP9RaB5WCyamFSszBRZaGhAFxGuQS7VgorVxyw4iqWnOmDmM5DzapI52SoGQn9vSoW9qYwv0fB4t5UCP8ev1KpjgauuhHc5CSckXVwRtejNjqO6oYxVqnGa6hsYqCqVi1MWsyFapGheM2OKKhEeZFHznf5fRKPrB+3Tw+lkZmfRWqoF5kFg5CHBiAMLYQwvAheug9eZhC6kEbJdLHBcLGpYmJUs7BhUsfrr9SwqbgZE4XV0KusAukV1oIyq5OxLaggBaIpqhnwkoJU/1YQU3kouTxSWfZ7ZXoULOxLYX5eweL+FOZlZAxnJAym2G+WFSj46hj46ji8wgickVdgj4+g9soEqhvGUBstozqiYXykhvUFAwXLwaQ1VSjjRDIQyMA5LshI7PcaTiM9lEJqKI/0/H6kFwxC6J8HYXgxSO88eOk+uJlBlC0PBdPFRI0BZ23VxNrxGjaVdGyc1FEtGjBqNrRXDJjPlf0y9mziMiam85BSeUjZPqQyMtI5GUMLc+jNL8D8HhVL+lNYkFcxP8t+s16ZR17hIVsVcNUxkOoknM1r4Y5tgD4yhtrmTew3G9ehrdBiy1jB8TDS5LqjAqjyHHI+pPskHqmcjFS/isxwGqmhDLZ673bILBiE1N8HYd5iCIML4GUG4GYGoXMKypaHouFiQ9nAqGZhfVHH+oKO1ZM1FAoG9KqJWtWCXtEbjIRtaPBGLXgbgzJWAVABJ2wEJ6yAIKsQUzlIaWYg5HQG6bwM1f/dFvalsLBXxbIlvThit3kYStdbLlmRQG3rixOmzvoMximle7ZaSQi5H8C8mFVfbTwlpYSQVoGuJZTSDYSQbQE8SAh5AUAp6QVG05taDCjYS2xMv+O4arLRQ8Wa5Xc6BkNJ9TBUEVTS4MFYQYpCTVDSkNJ5yPlBqFkVKR9oA74IbDuUxsKcgvlZGYMpCX0qjwzngquMgC9MwNm8BtbmtTBHx1FZO4LqhnFURzVoIxq0kVoIs8BJNcOMJwgrpcIxAeiTOPRKAtLDKaSH08gMpZFZMID0ggGo84dZpRxeDDczCC8zgCoVUTBcjGoWNk2aWL+mjNdGN2FzScfmiRqqRSOskGZpLBRJ2+9IbwYYL6kQ1QzEVA6imvF/m3QI+0AglwyksCCnYEFOwbyshLzMo0cE+MooeG0C7thKOK+uhjk6huqGMUxsGMPaEQ2VTVVoIxpKhoNJf9RT2YkHfSCMzLHz6JMk9Ek8MoOpEPKZhQNIz+tDeqsh8MOLIQwtAO2ZDzc7DJ1ImDRcjGo2XtMsrC3pWDtew/rRGjauqKFaHIFeWYda1YRVmYRZnYStlSNDT6fCPVpuMvO2CUGVzinI5GQsGUhj28E05ucULMormJeRkZd59Kk8hMooOG0S3thaOCOvwhrZhOr6MdSenUB1Uzk0Di9VzLDcaP5w4Fa/TTPQt90qi9SAiuxWGaTn9SA9vx+ZRcMQwt9mHrzsMEwxjUndxXjNwaaqiQ1lAxuLOl4bq2LjpA6tbKJaNKA/Z8KsFGFWX4et/S9sQ2v52wiyykJ+6TzkTB/kbA7pnIKewQGkcwuwZCCNJf0M5AuyCrbKyeiVefQoPCSjAL4yBjq5Cc7YBtib16Gy9u/QxwqorC+g8lwVtQkdlZIZCt56x8UrbnyYqRvM6dZoIkrpwa3WEUJGCCHzKaWbCCHzAYy2OMYGf/oaIeRPAN4N4HYAPYQQwW8dLASwYbrreVOLAdDYMijVbIxVDBQrJmpl9tErFRilMb+jsg67IAU39YiRwqr29CGdl5HJqxgeTGFJfxrbDqWxdU8KC3MyBlQB/SkBQmUUfHkVnFfWwNm8FuW1G1DdMI7y2nGU15VRm9BRLBoYM1khLTvuFNcfVN5ekUde5DEo8+hNi8jMzyC7VQbZhb3ILhpGZskC8IMLIC7cDm5uHtzsMCYMDxt0BxvKBtYUdaxeoeH1iQlsHFuLml9pa8UizOokrMpkbIWNfn85P4Dcgh2R6ulBKicj06Ngq8E0th3MYHF/Clv3qFicV9Gj8OhXOPDlzeArI3A2rYG9/lVoq8dQeWSEVdKNVby2qYqCZvvf3/VdfPz3zwl8CK7tB1JID6WQW5RDbvEAE73FCyBstQ2EoUVwc/NgZ4YwqTsY1x2sLujYUDXxykgVr41VMT6ho1rSob1qQn9mEmZ1I2xtRcvvH4BcTOeh5AeRyueQzskYXJTDcO9i7DCcxbaDaSzIKliYU9Cf8gWuvBlccSOczWthb1iFytoRaBteROn1ArQVGiobq5jUbUxaHgq2i3HHw0aP4omm758RWOulT2L/fzYvI+v//z3bD2Pxwe+COn8Y4sLtwQ1vDTczACvVjwkf2mtLOlYXanh9vMa+/3jN//91mJUijPIYbK3M+m1W6qAvuwA0AK+BlzaFjlvO9rEWSj6DTI+CTF7BkqEMlvSncPAuw6GIDag88pwNvjoGUtgAe/0qOGMbUF5toLK2iOqmCsrry9A21TBuMnEvh0OavQbTE95VLnAwRR4VmcfIQArV4RTKC3PILh5CZsEglEWLIAwvgrjTnpDz82FKWXA1B4Zmo1o2sHqyhtfGNLw+rqEwqaMyqUOvmtALo7BqJdhaGXioCw8nohSetUU6kO8E8EkAl/jTKTdJEEJ6AdQopSYhZADAvgAu9VsSD+H/s/feUW2f9x7/C5CEhPZCQiAQew9jY2PjvR3H2c1q0iad6cjtSnu7R26atje9vU3apmnTJM1sVuNsJ97bxgZszN4CIYEQktBAAjH0+0Og2E3a5nfS5nf7O33O8Tkc/JWe9fm83p/P83yfB7iO2BtF7/n5vyz/+mIQjcZPHAcWzhVMhxf/xfYIZhcygvcSgsVoN1muIVmpR6aSItdIUGsk5OhlFJnkZColWFQS9CkC1CIQ+BwkOIaYcVgJ2QbwD4wQGBrDN+wnOBLE4w7/VQFYjPplgkTUwqQYAFKEMfCnyVBm65Cl65Flm2POn5rJnDqDYJIMz9Qcdv80VluY/vFBepwB7K5J/AvLFeEJzwL4vfEN28XsZzG6jzm8GrFCT4pKhUwlRqFNIUOXQr5BTrZWikUlIV0hQisRIJn2IphwMOvsYLalj8CAjcCQk7ZBb8zhnSGcUzHgv1e0ejHsLSki9MlJqHQpyE0yFBlyFJY05JkGks3ZCDMLYkKnNOGemscVmuG8N8yQL0znSID+c0F8Hj8Bj5PgRIBpn4vpgIdIyP8uyCeJJHHIiZV6ZIZsZCoJCo0EjUZCvkFOoVFOukJMtkqCPiUJpWCepIlhGLcxO2JlashKoNeJb98ovkE/wbFJmr1TcXHz/xW4LS7l6ZOT0MlEZBmkKDLkKLNTkWemIrVkIUzPJUGXwZwqnUBiCuPhWez+afo9Ic67Q/Q4A4y6Q/jcIYLtU0yd8jLl7yQSOPWuzDYhMWlhr0CKSK6JC5omTY6sSE+OsZp8g4xMlYQcTQppslg2mxxyI/A5mB21MjPUjd/aQXDIia/Li384QNAVYnQhU+uanadxYW4Xy6KQxTKQJIziJJSpUmQmGaaadBTZsbkVpuciNFmYU6UzIzfiCc/inJylzxti0BuixxmkfjRAcGIKvzfMpNdHJOBhyuFipn+SuekJ5mfHgMZLMrHFuRUrtchUEuRqCTqthDXlRgoMMsxKCVkqMRpxbG7Fsn+EGMD8h3PO4KfA8wkJCZ8EBoHrARISEpYBd0Sj0U8BxcDvEhIS5oFEYnsG7Quf/0/g2YSEhHuAc8Ajf6/Cf2kxWPwbx1OzsSWi4PQsofDiZvHMO28NXbR+u1gWlz0WwSjVaONgzDHKyTfIyNdJyddK0UoE6ETzJHmt4LIxY+sm2DdAYMiJt9eFf9hPYHQy7jjemXcLwKLTLEZ/Wr0UhVmOMlOFKi8debYZQUYuSaY85pRpBJJkWCdnsfmm6OmYpNMxSr8riHtsEp87RMjnJ+wdfU/wL0b7Ur0ZsVJPslKPQpOCQivBpJdSlKagMFWGRS0hTSbCKBMi8I+S6LExO9zL1Lk+fL12bENjePsn8A8H8IRnGJ1a7Fsswl8sF4tbgUyETiZCkaFAnaNCnpmKMjed5Ky8WFajNjMl1eMKzWLzTXPKG4voOob8jDZM4vd0EvA0MO1zMeVzvQv0i6ImkioQyTUoMgqRqeTINRL0qVKK0xTkpsrI1cSyuNQUAbJZP0m+Uebs3cwMncNvHcF3xo6330twJEirOxzv219GrxcDTyNKwiQRkpGjQp4Wi9pVuemIzWZEliLmdRbmFEbGp6KMh2fpdodocgXpcQaxOgP4xkMEu6YInXIx5TvPTOhofO7+ll2q9FLkBTryTUspSpOTrU4hV5NCqlSAOmmWpIlhoiN9zI5YCfYN4Os7ja/fi++QLw501/QczbNznH4Pu1zMSoxiASpdCqosBea6zBjMc7MQmLJJMuUxqzYTTBDjnJyl3xvGOhGi0xHgzIifgCeMzx1i0uNlyu8i0usl0uxjftZGdN4at8uLIS5Rp8YEWivBbJCxulBPYaqMfK2UVKmQVKkAwYSdxAkHM9YOpu1D+HrtePuG8dv8+PsCjE5G4pnXYiDiB84lQFvipUHXP4Q5fDjnDKLRqBvY9B6/bwA+tfDzSaD8r3y+H1j+/6bOf2kxgNgSUXhmDk8wwph/inBgYQN0whODSdB7SUZwsUFK1EZkulTkGglag4yiNAXl6QoKdTIyFCLSpAIEHitYB4n0t+Lu6MNvHcHT48E36MMZiDA6Nfsu+Meiw0QUgiTSJQL0OgmKDAWaPC3q4iykOTmIckqZ11mYlhkYCc7S5AnR5w1xod5H32gXHmcQvztE2DtG2DvKdMB7CfAXoZGs1KHMLEahU6PSSzHoU6g0qyhIlVGkk2KSCVEmTCNwW5kd7CAy1IP38CCebgf+4QAtQ37eCM3EU/mLQbi4YW1IFqBPTmJZvgJNvgZldirqAjPi3GKEmYXMajKZSJDiDM3SPT5J8/gkF2wTDI0E8blD+I/5CL1sJxLoJDLpi8PvYrBL1EZStGnINRIspUZy0wqoMCsp0snIUolJkwlJmXSSNGEn0nuBYG8vvr4zeHtceE9N4HGHsYdjc9E5O8+5ub8U4ljkalIkx4XKsqUMZU4GwpxSEkz5zKkycE4nMDgxTb83RJvDT8eIn7HRIBOuSSY97pgAB73M2IPMD0bgwBiJggmEkgFEMnUsC7nIpirNKlbnaflkbRZZqmRSJUkIPFaijh5mhrrxdQfxdriYGPThG/Th8E/jmn53QLFoUwiTCIsFOHUSItkqAllq1MVZyPLyEOVVoKq9ApHUQHRyljFPCPtEmHNDE3TbffjGQ/jdIULuEaZ8rr9uU0IdKdPpyEblqKMyDL4UKoNiSo1T5GoSMcmE5IkTSJL4mJ1uY2qiA1/YjndkFE+Ph8DoJEOhGcYvOvG+WBZfcdaJkshMESI3SlHnqNAUmFDOpCNOLkagKmYuKYOJaSmOOS19MxLaklLpFPoZUgXxZgQJygKEUu1M+8YvEdR3MuDYXCzalFKbAndt+ODAiUL039dR/N8si+cLAlOzsVdJpxY2jUO+d2UECYlJcYCKFXqUhlTUBhnpBilLstQU6mUU66Wky4WIg04SuruJ9F4g0DeIu9WKu8eDb9CPLTSDc3oW34LDQgw6SmEsCkmXCDDqU1BmKdEWpqItzUGSk48wp5wZXQ7eaDI2X4R2W5A2Rz/NtgnGRwKxyMrliDvq7FQw3m6BREaK1kSKNh15qgGlNoU0k5xKs4pio5zSVBnpMiGymQmSxvqYsR4hcLADT8cgAwNePD0eHP7pS6L7uejislWs3cVyEUapCHWOCn1JLKJXlBQhyqtgVpNFIFmDPTDDmfFJ2kb8NHV7cR2fwOscJjC2AJigl9lw8BLAJMvVSNRGFAU5qPRSdGlyStOVVGYoKUuVY5QJ0SeGEYz3MzPYSajrKN76ITxPOHH3ejnriUXu45HYWYuLxUotjEW0RrGAmjI9uiItytx01GUFCLOKiZoKCcmMjE7O0DUeomnET+eIn/5hPx5nkGC7h5C7hUjw6CVBQ0yklCQrdaRo01GlyknLyaPYvIwqs4oinRSLSowxeY6kiWHmB9uY6m5lovsU7g47E8d9jNkD2MOzDM7McWFm/hKwa0RJqIVJZKYIUC+IU+b6QtaV5CC0FJNgLmZWnYljcpYed5gezyTnBic4bJvA7w4zMTZByG1numOcSIOP+dmzwNnYvUhSJSK5Gqk+E7lGgdogJTddScWyDEpT5eRqJBilAsS+YaLDHcxYO/G09uDu6Mc36MPd6WMoFBPWRRt3AOOJCTQugDxdIkCrl6LNV6MuTENdYMZ42TaSLKXMqjNxRpIY8E7R457kvM1Hh92HxxkT1bDbTtjrfCcw6IfEIRFJxyYRK6ykaGeQ6YZR6aVkZygoTVdSla7k2rI00uUCpKExEsetRLqdTHQE8fXZcXe68fZP4AjPXBKgLfqmJCmRo/8Q4kT/f3sdRUI0+s+87/WfW8qrqqP3v/g250f8XLD56BmawOMMEhhzEXLbmfKNxyOfRaBK1AZStOkodGq0aTLKLRrKM5SUG2RkKpJJTZpC4Ool0nsBf2sb7rYBPD1e3H1ehuIi8I5jXwwkszIZZZYSfUkquoo8UvILEeQtYSY1H2c4So8nzIXRd9rqHgkQHB//m22VqIwoDTp06XIK0pUszVJTaZRjViTHAOrsItLfhr+1jfEL/Xj7J/5mW5XCJEwLbVXlqNCXGNBV5CHJK0KQW8ms1sLodBI9njCd40Hq+9xxcAbGXEy6hoiE/HHgJyQmXQJNZaoGlT6F4iw1yy1qCnUyctRi9AmTCNxWIl2N+Frb8XbbcLW5GB+YwB6e/Zvjmi4ToclXoy8xoC7KQlZSHm+rYyqRbnesrQ0DHnqHfEy4JvGPjhL2jr5rXEVSJSKZGmlqJspUDRqDlJIsNUszVZSkyshWidHyTlsDXd242wZwtbnwDfqxTkbeV1t1hXo0pdnIyqoQZBUzm5qPIwwDE1O0OAPU97kZGgniHg1c0ta/DADECh3S1My4vZZkqanIUFJplJOtEqNJnEY41s10ZyPBnl7GL/Thah9nYsgfj849kbl3tVWfLCBTLkJXpEWTp43Za3E5Aksps/pc7GGwTkxd4lvvx17/L/rWYoASafpD49961fN9MSdVE919/V99CeiSkv+bFz5wfR9m+adlBgkJCY8ClwNj0Wi0bOF373nfxsKJufuBy4AQcFs0Gm16P/WEZubxhWYY808RCkwT8gWZ8i9EqAuOlSgQkSzXIFEbkBszSTUryclQsCpPxzKTkiylCPW0iwRbI1Otpxlu6GS8YxRX2zgD/ulLjDSWAcSWTjJTBKTmaTBUpcUMtHwpiVllBBVmrL4ILc4gJxvctPSdiTv9pGsonp4DCMQyxEodusIalKkaUjMUVC6AdEmanHSZEIm7l9muBgLtrbj29DB63slxe4ChUAyii2n4IuzNEgF5uhRWlugwVFvQlOWTXL6SubRiXPMSOsZDNAxPcHbAw+DgBO6GAJN7hgi7nyQy6bsEnGK1AUVaDmqDjJLyMury11NhVFCklZCaECTJ2cN0yynGmzpwdxzEecyF3RvGFp7lQmSOhgXHXsyasqVC0rJV6Aq1FN9Ui7yiGkF2GdOphQxMRGhzBTk94KF50MvYsB+vfTQOn9kTQTjhJklUj1jZh0RtRGlKJzVDQaFZxeZiA9/YlI9ZLkTmtxG1dTDZfAbXOQeu9jGcXW4GJmOA9M3MxTMjjSiJGbEQr1xEUpGW6epMdBW5iEuWo1q2g2iyHo93CrszSH2Pix7rBF5nEJ9jkJDbQWTSx/xs5B0oSg2khNPROLSYBCqqwxKWhycpT5VSlyZmfeIgn/GeYcLezpi9H1fbOCPOIEOhWcYjs/GIVpKUgE4Us7M0gwx9qY7USA4qcQnJqSuYERbiCAtpiVg4k6SkSbIER9oEnqibUGos+l4U7cWMIUVrQmnKQm2QkW9RsTpfT7lBhlItJnHaRdTeycyJV4meaUfa7qSw040kEME8FVtKXBwzpTAJnSg2n4ZCLfqSVPRLJEgri0gw5xNUmOnzRmgdC/DHMza6bBOMDfvxOewLwjfLXEQH6BBYYj6Qok1HnW6M+0BttoZKowyzXETyWBezA60ELjQxcqqN8T8eZWRggoHJmYUMJuYDisQEtohiPpCulmAo16Ndko62IhfZzXzgEo1Gmftw3ib60Ms/LTNISEhYCwSBJy4Sg/8GPBfdt6GORqP/mZCQcBlwJzExWAHcH41GV/y9OsqqlkR/9OQbnOj3cG7Aw9iQD699OA6PRQcVSZVI9WZkBjP6DCXleVpqczTUpCvJUycjcvUw03YKf2sbI2e6GT3vZHg8hC08G4+qFqFhSBaQqxZjrDKgr8xCW1OFqHQlM4ZCrIE5LjiDnOhz09Q7zrgjwMTwEJMu2yXASJZrSNGaUGVY0KbJqcjTsiZXS6VRQa5KiHCsm5m2k7jPnsd1wYqzxUX/WAjn9Cyu6dlLIGaWCMnUSDBWpWJYloe6sgxhSaw9/b4ZGh1+6gc8nO914xyaIDhmZ9JlYzrgiUNisT1KkxmdSUFVnpbVeVoqDHJylEJEjhYiXY2M1zcxeraf8S4PA+5w7NbJBaguLn2YJQIydCkYyvWkrShAWVaCsHQVYX0Bg74ZGhw+jvaM02n1Mmbz4XMMMOV1vmd7VBmZGDKVVFo01OVqqU6TkyFNRORoYar5OJ4LXYye7cfZ4mLAP41javY925OVJkdfqiNteQHK6qUI8pcQ0ubR7Z6m2ennaPc4Hb1u3KMBfPY+Qm7HJRAVK3VI9Zmo0tJIzVRSm69jWaYqJtaiGYSjHUw1n8BZ34Kr1c7YBRfWyci72rO4Tp6ZLsdYZUBXkYNy2XKSCpczqcig2z1FvX2C+j43nX2emOD8RXsWl91khmzU6UZ0Jjl1hXrqsjUU6VJIF06TZGtmuu0Mo6dbcLWO4Gx10Rt8J6OBd4QmWyokLSPWHuPyEqRlVSTk1zCRkkafd4r64Vh7ega8uGxeJl1DhNwOZiZj55qSRJJ4RqA1p5FuUVGTrWGVRUNZqhR9wiSJg+eZbj+D80w7I2dtuPu89AZj47MYyCxu9GZLYxv0qeWpsfYsqYXMcrziVPonpjk55OVol4thm49x+wQBRy9T/vF3tUduykNtVGHKVFGbq6U6Q8WuEuMHjtTL9Kroi1euf1/PFj/yyr9UZvBPXSZKSEiwAK9fJAZdwPqLDlIcjkajhQkJCb9b+PlPf/nc3/r+ssol0W//8TUO94zT1u9h1OrFZ+8h7HXGjUMglpGiM6EyF6AzKago0LGxUE+VUU62NIpg6BzTLacYOdmM87wDW8c4vcGZeIS2GAVZUoRkG6WklqdiWlmEsnY1CdlVuCVGmkcnaRie4GCbk7FhPx6bnaDTynTAw/xsJA4UmSEbXVYapkwVG4pTWWPRkK8Ro/QPMttyDO+5Zkbre3C2uOgdD2ELz8TXyBc33QrlIkzlqaRWZmBcXY2och0zxmJ6JiKcHJrgeI+Lzj4P43Y//uEuQm5HPAtJlmuQaE0oTdkYLSoqc7RsKNCxzKQgTTBFYn8DU+1nGDl+AftZO45BH73BCOMXCaI+WYBZIiA7Q4FpuQl9VT6KFWuI5i1nLCrjvHOS01YPxzvHGLVO4HPYLhGfRWeVGbPRm/Xk5WpYU6hndaaaAo2Y5LEuIi3H8TReYKS+l9HzY/QFIzimZuIgUwoTMYmF5MpEGKtSSVuRh2ZpBaLy1QS0+Vh9EY4PeTnW5aK3z4PL5iI4OkDY62QuEo4LslRvRmkyY7SoWF2USq1FQ5VBimHOA9bz+OuP4Trfg+OMg4FhP7bwO2K8CPc8mQhTlpL0mnTSVlcgLlnOfM4yRmbFNDj8HOoep7nfzah1gonh3rjwQQxcKVoTioxCdOkKSvN1bCjUU21SkK8SIRztINJ8hNHjTYw1D+NoGaMrEIlvzC5ujpslQvIWBNi4Ih/NylUk5VfjU2TR45nimNXDoY4xHEMTjA+OEHQOxIOlRfGVGSxozLEsa2OpgdUWDYU6CdrwKNG+RnxnTuE41clYyxgDo5NYQ+8EAovCkicTYi7WYagykbaqEvHSDcwYixmYTOD8aICDXS4udI8z7vAzYesmNO6IZ+9CqRKJ2oDKXIAhU0Vpjob1+TqWZygxS+Yv8VPHGRvO/omFsXjHTzWiJExiIXlpUtKqjRiWFaBcvpKE3KX4pWnoFNIPLgY6VfSFXWvf17Mlf3zt32IQ//J3i8FENBpVLfycAHij0agqISHhdWLvyB5f+L8DwH8uvEb1l9/5GeAzAGnp5qV3v3SM1y+MYO334BoYJjDSFwePcCEt1loKSM/VsK44lc35OipSU5CMdTF9di/2g2cYaXLQ0+O5xNllgkQsKULyDVJMS42YN1QhX7WJ2cwl9IUEHLF62N/upLfbjWtgMA7/6PwcArEMmdGCOiMHU66GbZVp1GVpqDCkIHe2MXXuMI5DZ7CdGGRwyE/fZATXdGzDdRF0hfoUMusyMCz68wJvAAAgAElEQVQvQrlmC3O5KxiYTOCI1cuelr/fX6NFxcYyI1sL9BRpxchdnUyfeZvR0y0MHRugr9+LNfRO6r/Y31y1mIyV6Zg3VCFbWsdcQR29wUTq7RO8cWGE3m43btsIfnv3JfVK9eZ4f9cWp7I5X0+VMYUUVzfTDftwHDqD4+ww/b1e+iYj8XoX+1tklGKuTSdtZSnyVRuZt1TTPyXiiNXLwQ4nXd3jjPUNMumyMeVzxcdZmmqO93dLRRqb8nTx/kaaj+A4VI/txCA26wTdwci7+punSyGj1kTGuspL+nvK5mVP6yj9fR7G+m3vOc6azPx393fBrpz1LdiO9dHfGxvni+3KLIn111hlIHNDJfJVG5nLXkZfSMCxwVh/OzvHcdtGCIz0XdJfmdGC0pRNep6GLRVprLZoqDJKkTvbmL5w7K/292K7yqg1kbayBGXdJubyV2INJXDM6v2rfnTxOBsylWyqSGNzvo4irQSlu5vps3sZPXme4ZPWv9rfAk3MrjLWViBbtor5wtX0hQScHp7g7dbReH8vtqu/9KO6Qj1bCvRUGaXIPL1EGvfjOFSP44yNvm7Pe9pVoT6FtKVG0pYXYPj6Ax9cDLSq6PM717yvZ0uffP1fSgz+P3ub6O/ct/G3Pvd74PcAJZVLopMzcwQWLqSLLFw5EV9ukMXSaZ1JwepCPetytDEhGGogdOYAtv2NDB4dotszhTUUITwXRZSYgCVFSKFcROaqdLI2L0Feu57ZwjWcd89wpNnNa412bN3jTNi6mRyzMRcJkygQITNYUGUWkZGvZWuViQ25WqoMUpIHTjN55EkG9zYwdHyYrokpbOGZeH1miZC6dAVpS41Yti1FtmIDM7mrOD8W5tV+N3v22xn+7X4mhjrjkX6SSII01Uxa+UrSctTsrE5nW76eArUIQfcxgvVvM/jwOWzHh3nYP40tPENkPookKVZfaZqMFTVpZG5eimzt5UyZKmgcmeRQ3zj3nncw3ODG//J5Jl2vMT8bIUkkQWa0oLUUkF+Tz6471rMtT0+eIoGkzqN4Du3FfvwtbC86aPdP88LULE8v1GdJEVGSLiO9xkT1f2xHsupyQoZizjqCvN7m5IVOF8M9Y0xYW5h6dJz53z+LQPw60lQzupxC0nM1fGpXMZtzVpOdMk9i+2E8Rw4wfKyDofoDtOydZjwyh20+ym7hAmwzFGSsTCdzywosX/0WAW0+9fYgb7Y7aeoYw9HrxDfUTtjrJNo+h3AwjHRPF6m5UfKKdOwoT+Pey4vJTo5Ae4jxA+dwnOzH2jhKq386DjulMBFjioiZTAVjG3JIW11Ncu0OFNu/yKQ9QH3rKOc6XTh6R/ANdcTBnizXIJ0zox/Io0SiYst0gPXZGj6Rk8DtkX7GBg5gH+vF2uqkIxCJ16cRxbLUglw1WfY8jKurEYq3M6IuprEgg9cjG2hUjDJmHcVn64iDdTH6T83NobREz7ZSI2vNKjImx7G0HSPl4BEKD3Ri6/XSEZiOBycaURK5UiH58xoyi/IxFC5HWJaOIzmZQ9YJXmuZ53xrGeNjWvySbqYyXQCIlXoU6QXostKoKjOiL06lNEuFfHqE2ZajjP74K4wf7kHY5yU3EEG98Afr9cmxsyqWfA2WTUWkbtSRWFrJcJKOs44AvzsxQFu7i/HBYfx2O9OBFKAQcbkepbmYVIuRpeVG6sqMLE2TYwgNMdtyDNfx+n8Mt4h+WLeWfujlwxaDv3bfhh0wX/Tc+7pLIxqFcGQufj317EWvBgolsat4NQYZVXla1uVqqTSkkDLchHf/qwwfbaX/6BAtvtgG8aKjFctF5FWkkrmuiNTtO5gv20S7b57XTtnZ02DH3ufG238+BpH5OcRKPbqCGgw5adRVmdhenMoqswKZrYHggSfofvMsAyeGaffHljoW66lWiSlYYsS8rgj9th3MlW6i0zvDL1pHOfz2KIMdrzJhbbmkHkV6ATkralhTZeKyEgO16TIk/ScJnHybgf9toPuUnZf97/RHn5xEsTyZuqVGsreXo996GXMFdbROwO7WEY60jmI7MYb3yZeZ8j0ch4bSXEx6YSY7t+3imso0atPliG2N+A68Rv+bv2boOQctvmkevKieMkUymWV6VnxlA9vXrCdavplz7jkO9I7zQoMdW9dIDL6/sBH9+a8RK/UL9WSwotTAj68rZ3n6lYisZ/HufZnhI60MnDhA894YeFuj4FwARcESI9nbKqj4/p2Ulm7CMDbF/p5x3m60Y+u04x/uio1b4xzi3glUey5gyHaypsrEdZUm7tmWR4q1Hs/bZxk+MkjfKfslgDeKBchlInxLjbjXFaPfehmar/2CkY9P09I+yt5GOyP943j7mwl7R4EY/FS+MtI6jKwTJ7K9eJI1mQq2TDcTcB2kv76R3lY7XYHIJfNTpkgmd1ka5vUlaIU7mSleT2vWNl5aXsGxFCc28wgT1ta4iEjURhTpBaQXZrBhiYntRaks18owdBym7sQBTG820dcwQsfCGRh4px5LsoFsXQWayp3M5RVxbnyG314IcaItA9vweiaSdUxnei6pJ6PYzFSpgYLyNIxpKQgH6vG+8BCjb54jes6JxT+NeKEeo1gQs4MlBrK3KdFsy2UmewVnR0Ls73Hxqz1d2Lsd+Ib6CHuTSEgsJblMg8pShrkonbpSAzvKjVSnihH2n8b91quc/8ljWJt/GhdERRSuFgtidlCThuW6KlRrtjBTvJ4z9iD7ul3sb7Sz57Xz+O3d8flJlmv+EfyKnTP4B9939H+lfNjLRPcB7os2kDXRaPQbCQkJO4Ev8s4G8gPRaPTvnp4rKq+KXnHv0xw4Y2Oke4CJoQ5mp4IIxDJUmcWYS/Ooq0zjo9UZlMumiJ7by/Arb9LzehctzkmsoUjc+atVYrLWmMm/di2itddhExp5tcvFiycHsXWN4eltYsrnimUARgvGwjJKy1K5dUUW67MUSKz1jL3yAtZ9rbScc9IdjBCcnUcmSKRMkUxBmZ78q5ai3XUDfmMFe/snePL0IL0dLpwd55l02eJOqM6pxFJq5JY6CxtzNGRODRE++ALWPWfoOjhIsy92JYIoMYFcaSzqzr+iDNNVVxAt20Sje46XW0Y5cMaGvb0Lv6OP2akgQqkSZXoB6SW5XFabyeXFBioVM8yf/DP2t4/Qu6ebC47gu8Ylb0s2WTtWkLz+BvqSDOzrc/PccSuDbcNxUP3luHx0eSYbLcr4uPS+ep6OTve7xqWkJo3sbZVod17HuKGKY0M+nqwfoqttjLGuFoJOa3xcdAVLMRfquGmVhZ35WoxhG+EDz9H70kn6Ttnfc1zydpaQfvUuouWbafREeabRzukLIwy3duIb7mYuEkYoVaLOKiOtwMy25WZuqDJRKJlm/vTLON4+TM8bnZyzB7CFY2JuEguoWhiX7Gs2IlxxOX1JBna3OXmjYZjBtmF8Qx2EvaMkCkRI9WbSSipZVm3imioTGyxKJB0HcR94i+7dTTS3j2NduNNfKUykWB4bF8vmcnRXXH/JuHQ0jzLe1/aucckuM/CRGjM787Wk+boJHXuV3pdOYj1tp2ninXGxpAhZkqfGsqmA9Kt3Mb9kJ01jUzzTaOd44zDOnn4mhjr++rikzDB/6iUGX3oL62Hru8ZlqS6FrLVmcj+yGUHdtVijav7cNsqeJjsDLTa8/c1MBzwkCkTITbmk5hZRsyydW2rMrEiXIek4yOirLzOwt53WDjfdwWnCc1GUwkQKZCLKlqZRcM0KlJt2MW6o4u0+L39uGqajeZSxrnOE3A6i83NI9WY0ORVklxm4dlkGW3K15KcqPvCyTYlaGX1mY+37enbJS3v/pZaJ/plvE/2J2B9n0AFO4AfAy8DzQCYL921Eo1HPwv7Br4HtxF4tvf299gv+shSWV0U3/uBxjp224ezpwD/cDYBEbcBYWkNNTTofqc5ga7aShPqXGNmzl67dLdTb/DinY9FMgSyZcouSwmuXkHrZTkIlW9jT6+HZszZaGuyMdzfGnVqVWYyxMJ+tK7P4+DIz+clBZg89Q89zB+g/OsRZ7xSeyBySpIQY6KoMFN9Yh3zHTdil2ezuGOPZIwMMd9jwDDQzM+lDKFWiya4kp8rCNbWZXF2cSvrkAP7Xn6bj2ZN0tIzR6o85hEaURI1aTNGOXLIuX4tgw800BcS8dGGEvacGGW65QHDUyvxsBInaiL5oGWXVJm6sMbMjT0NK+z6cr79G9+5mzvR5sYVnALCkiKgwySi8uhzT5TuYq7mKff0TPNtgo/GsHWdHA2GvEwBFRgGG/GLW1Jr5xPJMKlRRosefo//ZN+nfb6XBG2Z0ahZRYgLF8mRKCzUUfaQG3TW3MqYt5vWucR4/0s9Qxwie3iamAx6SRBI0OZVYKvO4rCaDGyrSsMyOEt73NF3PHqP77AhNE1NxWFYqxZRuyCL3ypUkb76VznkNu1tHefW49RLIJ8s1aAtqyKtK5+baTC7P16K1nWb8zZfp+nMjDQsQjsxHMUuEVGklFF5ZROYN1xCt2s6x0RmeP2fnxGkbI+3nmHTZAJAZLOjzy6ipSedTKy2sMElJqH+Joed207e3jzOOwHval/EjNxHIXM6bPR4eOTaAtX0MV+eZuJiqMosxVxSzeVkGt1RnkC/0M3voGbqfPUDv8WGaJmL2tSik5asyyN1Vg+yyj9InMvNGl4sXjlkZbO6OB0aL9pVbnc11tZlcVajH5GnF+9af6XqhnqbzTvomY0ukRrGASmUyRTtyyb7pShJrdtLkF/H8eQcHTg1hb3vHvqR6M9q8SsqqTXyqLpt1WQrELW8z/MKf6dvTSYN1AmtohqSEd+yr+NpKTNddR6RkE2/3eXn0pJXuFifOjgZCbkdcIDLKyqirTuf25ZmUS4LMn3ktbl/1nhCu6ZiPFciSqao2kLerGs1VtzKsKOCtXjdPHe1n4Hx/fJlMIJahMOXifOnLH1wMVIro0+vfnxhUv7Lv32LwYZWCsqrosm88zNn6YVxdjYTcjnhWkFNdxCc25bIjT0vq8GlsTz5B7+vt1A9MYAvPIEqMAXvpajMFH6lDvP02uuc0/P70IAdODDLS3kxgpC9uoFlVFdywMZerig3kTPYw/sJjdO9u4myri77J2NJUgSyZygINpR+tRXPNbQxJc3imeYSXDvdjb+9iYqgjHrWkVyxlxxoLN1enU5rkJvTGY3S9cJLG0454NGSWCFmWIafk+iWYbrkNj2kpL3W4+OOBXmxtVty9TcxFwkjURoyly1m5MpOPLTezWhdl/vTL9Dz+Muf3DdDqn8Y3Mx+P9EuuLSHzozcwt2Qnr3V7+O3BXgZaR3B1nmZm0heHaG6Fic+uz2VXgYakc28w+MSf6Hi5k7Pe2EV8i2Cu2JpN3q1XkrjyGo6ORXnqrI1Tp4YYbTtD2DtKkkiCNq+arPJsPrYhl+tK9KhsZ3A88yRtf2qicSSILTyDJCkmINUr08m/diUpO2/nwoyWp5uG2XdiEPuFRiZdNhISk1BlFpNeUsg163O4pcpERrAP9wuP0vb0ac4tbFID5EpF1JTpKbx2Gdrrbqc/JZc/t43y4uF+Bs9fIODoIzo/h9yUi7GwjK1rLHxqRSb5CW7Cbz5Gz0unaDxuo9U/HReNFdkqCq4oI/2jtzCaXsuennEeO9jHwLmuOIQlaiOpxTXUrjRzS00m69MEzB98nL4X9nNh70Bc3PTJSdTqpRRcXkDWLdcTrtrF/n4vDx7qo7/VyXjnaaYDHoRSJbqCGkqXW/jYyiyuKNCSdOp5hl95k7bnWzntCeOJxOakRi2hcGMW+bfuInHl1Rx3J/G74wOcb3RcMieanErylhVwY52Fa4v1aHoPM/rqy7T9qZF6mx/H1GwcuktrTZR+fCPJ62+gPZrKb09aOdlgx3a+gaDTSqJAhCKjgOwl5excmcmtS0xk+TrwvPEcrU+cpLnbQ3dwGoiJw4pSHaW31qG+7Ab65YU82WTnzdNDDJ5vxbfgJ/K0XMxVS1i9NIPPrsqiSOBjas+jtD95hNbGEZp9sTkxiQWstCgpuXEZhquuZdxSx4vtYzx33EpfQ2d8TmbOP/YPEYMn1/zdt94BWPb6/n+LwYdV8koro7lfeJC2E514+puZnQqiyCjAXFHJrdsL+HhVGsruQ/T97hEad3fGHTBXGjPGys9sRHL152nwJ/Pf+3toabAz2nqcmUkfKVoT5uqV3Lgtn5sq08gaa8T6u4fofKmdo84gvpl5TGIBq/M1VH5mHaorP8Z5zPyhfpD9h/oZbTnBdMCDRG0ko2ola1dl8uW12eT72xl99nEuPHaao8N+PJE59MlJrM1WUXRtFaZPfoFBeT6/PjHImwf7GG07S8jtQKzUoy9azpq12XxtQy7FjOF99kGa/3A8LnAaURK1GgnlH12C+aYbGc1ex0P1Nt44bmWw8SxBpxWBWIa+uJbatXl8ui6bdaowwRcfpP3pExw7N4o1FINyrUZC+fZc8j53G56S7ezucPHIni4Gzr7j/LqCGvKW5vC1bYVsNUaZPfgULQ+9QdPZEVr9UyQlxAR3xZZsiu64iflV1/N8m4vf7ulisDk2Z4tgz6+t4M5thVyeryHhwCP0Pv0GZ97up9k3zVw0SrE8mZVrMyn+2BaSdnyW3X1BHj7ST+epDty9TUTn51BkFJC3opqPbsjhlnID0ra99D70KA2v9dDsmyY4Ox8Tm1IdS764DdHOOzg+nsDP9/fQcqIbd29T3IYyyiv46LYCbig3Yho8zsDv/0DHy52ccIfwzczHNv1LdFR9ZgOyyz9Ow4ye+w70cP7M8CU2lFFVy6a6LO5cbSHbfZ6RPz1B86P1nBibxBOZwygWsDpHTclNyzDcegdtQgu/PzXIvsP9cXCLlXpSS1awcX0OX16bQ8GsDfczD3H+Dyfi0NYnJ7FCk0LFx5eR8anPMags5renhnj7xCBDTfVMumwIxDIM5WtZvdbCZ1ZlU5syge/5B2l/+hTHWlzYwjPIBInUaSVU3lSJ+eYbGcvbxIvtTp54q5uBs2cIOq0kiSSkltaRX53J17cUsEEbYfbAk5x/cA8nGkexhiKIEmM2VLolm4I7biWy9EqebhnjD291YbvQgdfaSqJAhNpSRkFtCV/dVshWi5zo3t/T8tBrNJ52XGJDyzdmUXjrdhK23cELHW4eOdJH18kWPP3NAKgtZeQtr+C2DTncWJaK4OiT9D75CvV7+uKZ9e8Y/OBioFREn6ireV/P1uw5+G8x+LBKTmll1PyJ++k43oRvqINEgYjU0joqarP54Y5iquasjP3pYZoePBYHeK5UxLrVGZR9cjuJl9/J4xfGeGxfDz0n6wmM9CEQyzAtWc/KVZl8Z0sB+d5m7I8/SuNjZzg6HiI8F6VMkcyabTkU3nYFU2s/zkNn7bxwsI/++lNMumwkyzVkLF3L1rXZfHVtNqb+gww+/hSnnm3hrHeKuWiUapWYVVcWkPuJm3GX7+KBE4O8fmSAoYYThL2jSNRGLMtXc9WGHL5Qa0bZ+Gf6/vgCx1/rodkXc5JajYQVN1eQddttDJnruO9wP4eOWbGfO8Z0wIPMYCFnxQpu3pzHp5eaEOx/mM5HX+fowUE6AtNIkhLYoJey5JMrSL/t03RIS/j1iQH27u9lpPlIHIyFdcv55OY8PlqsZvbV+7nw8D6O1DuwhmZQChNZb5Kz9Ivr0d7wKc7Op/PAkT6OH+xkrO0E0fk51JYyilZXcefmfHZlJDL57C9p+eNRDl8Yi4NsXa6GZV/agvTKT3NwQsr9h3ppPtLKePdZAHQFNVSuK+NLG/LYqAzgeeKXtDx+miN9HlzTc5jEAjZUGVj65csRbPskLw/N8uCBHjqPn8fT3xwHWN36fP5jXS412HA8/ACtTzVw2BGI28balelUfOk6EjbexlOt4zyyv5eeU2fxD3cjEMtIq1zH1s15fLEum+JACwO/+RUXnmvh6HiIyHyUAlky67ZYKLz9CiIbPsEjTQ6e2d9L36mTcdtIX7KG67YX8ImaDDIHj9Lz699x7vVeTnti50HKFMms3pVP7iduxLvkKn59cohXDvVfYhuZy+r42M5CbqlMQ3P+ZTp+9QSNhwZpmphClJhAjVrC8hvKsNz+cWxZa/mfIwPsPzoQtw2p3kzOipV8blcx15XoER99nOb/fZ6zZxy0+qdRChOp1UhYevtyMm7/FJ2Kcn6yv5tTJ4dwnDsct438lTV85fJirshXM//qL2n85eucbolly/rkJFYZZVR/fi26Gz5FU0Imd7/VyYXTA4y1nWB+NoIys5ji1dV8c2cxm9KSCL/4Sxrv38vxHg+OqVlMYgGrctQs/eImZFd/miMBBT9+o4Oesz1x29DmVVO2ppQfXFZETUoA7xP/S9Nvj3LC7o/bxkqLkq2dTR8YzsVKRfTxle/vK1a8fejfYvBhFUtxRVR3/X30njhGyO1AZrBQsmENX7mskCv0YUZ/fS/nHqln/9gkSQlQq5FQ97k6DF/4Dge9Kdy9u5WOw8cJOq2kaE1Ylq/iK9eWc1OOkNALD3D6Z3vYP+QjODtPrUbCutuXkXXHFzgjLOCbL7XEQSNW6jFXr+Yz15byiSVpJL7yP5y655U4qCqVYlZtzab0O1+hI3UFP9nXzZE95xjvjl0slrFsM7deVcIdyzOQH/sjF+5/kf0nhrGFZzBLhGxbn0nFdz6Lu3Qnd+/v5c032+KgTVuymXXrc/jRtgKMzS/R8Zun2ff2AH2TEYxiAVuXm6j64uVMbf8i9x21sntvD0NnDjAXCaPNq2b5liX8dFcJ+d5muu79b0693sNZ7xQaURLrspSs/NblCG/6Nk9fcHL/i61YTx9iOuBBZSmjYtNyvr29kHUCOwM/v5czz7Vywh1ClJjAplQZtd/civLGO/nziJD7XmyJR5bytFzyapfznevKuUwdwP3kA5z8xUEOuUIArNWlsPTWasxf/ib7gjp+tqeT1oOn8A93I1bqya1bz1evLef6AjmhJ3/C6Z/t4bAjEJ+n5dcUk//Nb9Mmyeeu3a00H2jAa21FIJGRXbuJm3YU8KWVZth9H00PvMW+Zieu6TkKZCK2XpFP8be+yoBhOd95o4NTh7twthwlSSTBUL6Wq3cW8a0NOSgaXqTpnkfYd8qOY2oWS4qQLRstlN55I+PLbuA3p4Z4/rUOHOcOMj8bIbWkjq07K/nGxlxyBg7S+pMH46JsEgtYW6pnxXevJ7TpszzcYOexl9pwNB9nOuBBV1BDyZpyfn51GeW+8ww8+CAHn75A08QUSmEim3PV1H5rF6Kr/oNH2/385sVWbE0nCXtHkaflUrJhFfdeW8FK8TjD99/LsUfOcNY7RVICbE6Ts+Szdeju+A4v2uCBNzrpOnqMSZcNqd5M4do1fOGyIm7IhPGHfkzzH06yd9jPXBRq1GLWfHI5GV/6NqemdHx3dwutB04QGIldFZJXt5ZP7SziE6UqZl59gFP3vML+Pi++mXmqVWI23FhGzp1fpEVZxV27W2k/1sJ491mS5RpMlau5/ZrS2Dy99Vvq73meo20uHFOzFMuTWb0uk4rvfoH+7I38z+F+3nqzBWfLURIFIkxLNnL9rmK+sDITXcNztP3qWY4fGeLLge4PLgYKefTRZdXv69lVh47+Www+rJJZXB5V7vop/Sf3MzPpw1C2liuvquYbG3LQn3yc0997goMd43gic9RpU1j58aVkfP1unhkW8MDLbfQci4FNm1fNpl01fH9rIZmtu2n4/sMcaBzBMTVLjVrM2hvKyPn2D3jVp+O/X22n4+AhQm4HKksZK3as5H+vLiPLeohz3/slB4/ZsIZmKFMks/7yPEru/gEH57P5r1fbaNl3NA7Diq1reOCGSkp8F2j5zj289VY/1tAMBTIR69ZnUXnP1zmnruG7r7fTuOcY/uFupHozpZvW8/MbK6mZt9J99w/Z82In3cEIZomQnTtyKPnqJ+nN3c6P3urkyGun8PQ3I1EbKVq/gbtvqGST3Mvgz37A3sfP0+ybwigWsGO1mcqv34Kj+np+vL+Xt145i6vzNMlyDbmrN/LtG6u4yjSP85c/4MBvT3LWG0YpTGJHtZGld12Nb9Pn+NabXRzc2xYHZ/bKLXzt5ko+Wqhg4rff4/DPD3DCHUaUmMBlxTpq7rqMuWu+wQ/29fH6293YG/eRKBBhXraRz99YwaerTUz/8Yccu/t1DrkmAdiWpaLmK5uRfvJufnJ0kOfe7GLw9F4AjJUb+Ni1ZXxrbRazL93H8e/v5pDNR3guyuZUKcu+sAbdnf/Fb1v9PLy7nf4Te2OQLq3j6iur+NGWXJIP/oFT332KNzvGCc/Ns1aXworba0j/2o94ciiR37zSTveR/UQmfWjzqtl65Qp+clkhmobnOfO9P7CnaRRPZI5ajYTVN5Vj+daPeMWj5L5X2uk4eJCwdxSVpYxVO1fxi6tKMfcdoOm797PnuI3RqVkqlWK2fryK7C/dxb6ZTO55tY3WfYeZdNlQZBRQuaWOX11fSaGniZbv/YQ39vRjC8dsZsd1RRR87UuclS/hrmeb6TxeH7eZsi3r+cUNVSyd7aPzhz/ird1ddAcjWFKEbN+eQ8ldn6HLspkvv3CBtiNNePqbkRks5K9ayY9vqmJ9yjjWn/yAt55sptU/jVEs4LJ1mVTedSu2yuv4yu5Wmg614Oo8TYrWRPaKOr5/UxVXKMexP/gL9v3u1IJwJbGzxkT1XdfgWfcZvvlGB0f2d+BsOUqyXEPmsjV8/aZKbs6YZfzRn3PoF4c44Q4hEySyvVRPzV07iVxxF9/f28sbb3czcm4/SckSMpas486bK/lMsZTAU/dx4sd7eNsZRJSYwPYcNcu/shXpzXeRrNJ/YDgXyeXRR6uXvK9n644e+5cSg3/pK6zn5yESnmFuOnYIS5WmZ0OBjrSIk/5XD9DQ4zy6rwQAACAASURBVMU1PUexPJnaW6owf/mb/Mku5KdPNTF4ej/R+Tny1l3B5z5SxmdzE3D+5qs8/sAxmiamMEuEfPHmUorvvZdDERM3/r6RzkOPMhsOYlq6je//4HY+v9SI5xd3sbfoMD/3hDGJBWxbbWbpz7/FCVk1n3uphQuffpkpnwtdQQ23fe4afrwtj5k/3cuBb32Oh746iUyQyJUrM/ja4f+hx7yezz3dxL2HT+O/7Vl0BX2s2bGU1qfuRPZ6LNt47dtPsPt7CQTztay+7xPs+tnTfPP1dv78ylF+1N+B4qf9rL6ynZ/uKsFiPM/Jb7Ty9vHDhI/8CZ5SE/7uFcjvfoRjS9s5succrs7T/EqYS0lzDg+VR/ldvoOzzqd5rX0YT2SObd172GC5kvlbv8evVnyVZwZW4eo8jVip5+i2LdxdWca64dN8/okfk3NkKC6g27dPoytay0PNbh7oX8+weQpBgYyCdVvZdGs1KdIxuj5/A6kvdrIlPEO1SsyOL9RhvLOSPwwksPx7++k7FiDBspHM6zfxrY9Vs9kYxPa/9/InYxVO/zSflIrYdWMJeXf/jNc8Cr7/RBP3/fBB5mcjmLd/n9uuKeW/lmkY/enXOfDLw5z43jIsKUIe32hhyeM/5M25PO75cwuP3f8UD93jwVC+ltvve4IfrzISeOy/OHDPHu677wia+zexa30Wp753B+13fJcvPNlEx+HjPPP/sPfuYVVXef/3QuQoKOLZrM0ZxEM7JKLwEHkguinNKTPHxjBsO1vhwSiUwUEZmSgM0x9FcYeSJKkjoYQCioIRIioigghxRs5n2LA5O6/fH1/93tOc7p5reua+7nmm61pXKNu192e9v+vzfn8Oa+39n4hLaUvFqv9Qiv0XMsT8Y78TeR+mibR6jbgTky+88jYLr4+2C6etL4mt5kYi/+xl0VN7R+SeE2KP4Xjxu+eXi4XbakR3dby4eK9XlPcPC/usajFrcaaYu1wt7B41ExWm5kLbXi/+OCoVxPV1dcT97jahaegT3aP3ha6OEPZmhuKxVS5iwOoZcTS1XFQXlMlEYL90idi/7nGhbP5O3Ar7VKRcrBHtw2PCbYqR8Ni1Sphs+1Dsz70n/jP4nGi+dVEYmJqLx9esFwfeWCTcqBJFgRvFxw8iTVdzI7EzYIl4JHi/OFTUL36VVCJqg94TugZGwtJ1ufg4KEz8YmKbqP4wTKQ8XSS29Y+IuaYGwsv7CfHmu8HiZMdk4RNfIO59lCX+GB4oLJ7xEAFb3ITabpm4FxYoLh9JFlcjh0Snib5Y6WUjXrvxtRivmS4+PFUkPsk6L8YO1IvZ36UK9S+VovSgp+g+lCXOf5Qu8q6cFl2x48XdF6zF/N07xOQru8T1r26Kiu+zxaedTWJatpVYNqHm53E6CMH9/70C+u/997+aDMb++Ecx0NMj/jg2IkxnWYtFylniBVtz0fNZkPju1F1RpR0RTmaGYk2wh9B5O1z4nC0TZ48fE5qGcjFj/lKx4+0lws9qRFSFBojwr4pE09CY8JplKt74ylc0u/5KbIrLF4VvSDd52ix7SSTG7hIe42tE3luBIum5BhEE4hXXOeKNK/8p5ug6CP+Ya+KDKxeEzrY0ofQaL/7PL53EvAUV4tKWs+J84UUx8YyuqF3vKOx+93vR6OAtUmJyRMvtLBE7wVp4lM4SB+x0xTd6KeLStTSR1T4gHq/JFc8/+qQwW/e52Kb7okhbOE10GN4Q0x3dxOi6p8TSZ6YLEeQt3I4ViWn9I+KFmSbiWb85ovul+WL7N8UiJ7FRDOg9Lma/6SH2qp4SK82aRXHQHpH49kIxY+yP4ktbc7HsP98TV2evEO8eKxBPrt0t+ON94bBil/gs7inh3HFVfO/zOxHy6+PCaPsJsW6FpQj/7ID4ptdTBMdcEzf+8LV4+fw04bzGUxw7kSLePf9/RKrfMZHVrhXtEZliXc+vhTrofdH18jzxRXeb6Ci/IRrvlor00jlisYeNmGQ9W5iMf9AOrKsjTB+bIbQTZojzd4pEa4XU6z57kYd446W5YsMjI6J0R4D4NrlCNA2NipdtzcWS8A2i1u1t4faf10RppqTYHVa+JPZseEK8OHpbFARtFb+5VCt0dYT4jydmitcvHRIpugvE5mO3xA+//EroT5gkHFesFHkngoXF95+L7F0HREp8t/jYSE94rbETa4vPib66cSLsP/PEhzfPC5N9ZcJ9nUKk+j4tML8ozgRdFXmFmWJusoEYDHhWmLx7UJybvkH84dhF0VN7Rxy3dRWqUWex0/CPIvqHKHHqu5uidmBUeDRfEx4bxkS37lzxQu0icfPRFjFs1iUsnvkPsfhtF2Fg2ibaNq4Wj2bViXUj98XaJ2aKp99dIm7ZzhOvfPy9qMi+KhjnLBb89kUR8csnhGv9eXH57UiR/sZhMXX8OHH+ZXsx75vfiS+7pouIr26JZWt3CuMps8VCj2Dx1VFnMen0B+K73WdEyHvJYu7v0sW6Xy4Q7338uXgvc4H4JvG6uH3mhHjtbpXw+sXTIio+Ucz4P7vENx9cFHldg+L+kRtijUWk2Lhxr7hV1y3afpgt+ltrRV+XRrRph4WYrCvGhkaEZuyPQn+cjrAzNxSP/MdKUW1gIT49f0Pcu5El7g8PClv3F8Wht13EspESkftSsEi60SR0dXTEJneFeOqL/eL8qEKsO3RNVGd/KvQnTBJOq18Sn/7SSdjmHxWXXg8VAS19YobBePHaGwvFxr3h4uMKXfFibI5o3fwHMXFOoVj9Sw+R/s5vxNhnO8Wl/QdF9omBn8nr/OueQBbA/9oxzcqRGWsPoqf05tFN8XyZf4+x4ovkui/Fd5wFoYbWFG94geHuVracvMW0NZHoKb2x236a+Jv1jBakccHBCd9xFoQZ2XD3zRcZ6u1kU8JN+bX2fmc4XtjAyPVk0m2fwHecBeHGNtTtepOhvh5eP3ods5Uh6Dv54LjjWxKLGhnOTSTNSolaR0G4sQ31IT4MavtZeziPScuD0XfyYV5ACtfruhjOPk6qYiEqoSBigi2N+7YyMDjI6i+uMtE9CH0nHxbuPMethm6GLh0lZdZ8VEJBpIktLR9up6GrH8/PrmC6LBBDFzWPB52jqKmHwfOxJM+ch0ooOGhqR+t+P+o6+1ge9T2mywIxcvVF+ZtUSpp70RzdQ9IMR1RCQdREO9oPvUN1u4bnDmVjsiQAYzd/FoWkU9baS++R33JquvTahKlz6YreSUWbBpfQCxi7+WPs5o/z3vNUtGnojgkiYepcVELBiWlz6YkNpqy1l0Uh6Ri7+WOyJIBnP/6O6nYNHVHvEjXRDpVQcGq6I5qjeyhp7sVpdxpGrr6YLAlgedT31HX20XbAn4Om0muTZ85j8HwsRU09PB50DgNnFabLAvH87AoNXf20fLidSBNbVEJByqz5DGUc4VZDNwt3nkPfyYeJ7kF4xeSiHRikcd9WIk1sUesoODtnAUNZx7he18X8987Kr117OI/BgQEa9r5NxATptamKhQznnCSpuAnHHd+i7+TDpOXBvH70OkP9Gu4Fbybc2Aa1joJ02ycYyTvNyduNOPgno6f0ZupLEWz86gZDmm7uvvkiYUY2+I6z4IKDE6P5Z0koqMfe7wx6Sm+mrYlk8/EChnvaKXnDi1BDa3zHWXBl6RJGC8/zZf49HvNOQE/pzYy1B9l6qpDhrmaK1nuyx9Aaf10L8lY8y1hJFl9cq2XOxjj0lN7MfCWK8MxyhtvrubV2FYF6lvjrWnDNw537P+QQfbWGRzbEoqf0Zta6aCKzKxlpraFg9UoCxlsSMN6SfK8VjDRXEpVbzez1MegpvZm9Poao3GpGmiu54fmc/NqC1SsZaa0hMruSWeui0VN688iGWD7Pq+X+Dzlc83DHX9eCPYbWFL76PMPt9YRnljPzlSh5vx++UcdYSRa57kvx15X2e9F6T4a7mtl6qlD2DY95JxBxuQIhRP4/6nPsjSfwncvTP2n8I+8nhDAXQmQIISoe/H/yX3mNuxCi8E/GkBBizYPffSmEqPmT3yn/2/f8n3bo/8gwt5yLuWcYBs4qFu48R1lrL20H/Ikxs0clFGQtcmW08DxRudXyg2G3/TRXajoYyjjCiWn/5aiGLh0lu6oDG3WSvDmir9YwWpBG1iJXVEJBjJk97YfeobSll4U7Jcdj7hlGcOpdRtrquObhTsB4S/YYWlO36020A4M8dygbI1dfjN38WfVpDoPafqoDNrLbwIpAPWkDDXc18/rR60z2CMXAWcXjQeeoaNPQut+P6En2qHUUfOfyNGMlWRzMqZJtsfc7w/W6LgbTYmSne2q6I0NZx7hc2Y71r7+RN+/nebWM5p8lU+mCSiiInWxPR9S7lDT3Mi8gBQNnFVO8wtl8vICR1hryVjxLwHhLQg2tuRe8mT7tAMsOXJZt8fzsCkP9Gqr8X5dtKVi9kuHuVvySipjsEYqhixrlb1KpatfQ9L6aqIl2qHUU5LgtZqw0m4jLFTLpOvgnk3+vm8Fz0cRPcUAlFCTNcGQ4+zgZ5W1YqRJl53L4Rh0jeae5tOBJVEJBnLkD3TFBFDX1yM546ksRbD1VyEhz5Y+cRcPet+npH2DpR1kYuqgxWRKAV0wuQ5puKra9SpC+FUH6Vtxau4qh3k62Jd7GbGUIhi5qnHanUdvRR+O+rRw0lWy5snQJ9yuuEp5ZztSXItBTeuO441vSy1oZSD5EnLmDTFrDuYmkl7ViseWUbMuX+fcYzk0kw9FZtqUnNpjbjT0sCkmXbdmWeJvRpnJy3BbjryuJl8Z9W+nu07I4IlO2ZfUXVxnq7aR86y8I0rfCX9eCwlefZ0jTzdZThUxaHoyhixrnveep6+yjYe/bsi257ku5X3mN0Atlsi3zAlLIKG9j4PQB2ZaUWfMZyTtNyt0WFG+dkB15QkE9w9nHueDghEooiJ/iQO+R35J/rxvlb1LRd/Jh2ppI/JKKGG24S7brM/jrSuKq6X01HRotbh9ItpguC2Rd3DWGu1sp2/IygXoSiRSt92SoX8Pm4wVMWh6MkasvLqEXaOjq517wZiJNbPEd94Dwqm8Skl4qk83PQQZ2xhPIWuT6k8Y/SAYRQohdD37eJYT48CeQR5cQwvhPyOCV/1fv+T/t0P+RMfExe0yWBGC2MoQtJ28xVn2Ts3MWoBKSshurvknYxR8wWxmCyZIAVn2aw0hbHVmLXFHrSMp25HoyX+bfY9qaSPSdfHD9/UUGBwYofWs1/roWRE20ozsmiIzyNllFLdx5jqbufupDfAjStyLMSFL/d5okktBTemOjTuJSRRvdMUGEGdngr2tB2ZaXaerux/X3F9F38mHOxjjib9YzkHxIdpT5XisYbq9n1ac5GLqoMVsZQtjFHxjJOy3blrXIlbHqm2w+XoDJkgBMlgSw5eQt7lfdkInroW0PN7ahixrPz64wODDADc/nUOtIUcBAyid8mX+PRzbEou/kw+NB52ju6af0rdWybT2xwdxp6pXJxUadRElzL/eCNxNqaE2QvhUNe9+moatfVtFzNsZxqaKNgeRDHDS1w1/XgnyvFQwMDuL6+4sYOKuYtiaS+Jv1DOcmEj/FQSa94fZ6Nh8vwNjNH7OVIWw9Vcj9qhuy/amKhYzV3CL0QhmTlgdjsiQAz8+uMNJaQ6bSRcZ28Fw0h2/UMfWlCPSdfHgm/BKD2n7uvvkivuMkbHtig0kva5VV70P77wVvJlDPUratqKmH+e+dle2/XNlOV/ROQh+o7bItL9PQ1c9T+zJkBZ9QUM/A6QOybfleKxjubGR51PcYOKswWxkiqfHcRDni+87lacZqbrEp4aZs29ZThYy0VMtEfmLaXEbzzxKSXipj6xWTy6C2n2se7qiEguhJ9gyei5Zte4hta49kf6CeJeHGNvQe+S1FTT0y2dptP01pSy91u96UsS3f+gvqO/tk+x/dFM/lynYGTh+QsS1YvRLtwCBP7cvAwFnFjLUHJXLIOSmT+3cuTzPc2cimhJsYu/kz2SOUbYm3uV95TbY/zUrJWG0hIemlsv1eMbmMtFTLtp2YNpfBtBi+uFYrY+v2QSZD/RpK3vDCd5wF0ZPs6T3yW1JLW2TbfhYyMDLm0oInf9L4B8ngByHErAc/zxJC/PDfvP5tIUTCn/z5/19kYDLHDkMXNbPWRZNQUE/vkd+yx9CaQD1LWvf7kVHexmPeCeg7+fDcoWyG+nr4zuVp2aGMNpXz3KFsDF3UzHwlisM36uj/eh+hhtYEjLekcd9Wsqs6sNhyCn0nH5YduMzgwAA5botlhTRafwfPz65g5OrLtDWRRF+tQZu4n3BjGwLGW3IveDPX67pkR+r2QSZ92gFy3ZfK6nes+iarv7iKsZs/U1+KIDK7ksFz0URMsMVf14LqgI3cbuzBbvtp9JTeuP7+Ip0aLTc8n5M3x/2Kq7x+9DomSwIw9wwj7OIPDGUc4aCpHb7jLKjY9iolzb1yasJ573lae/opWL1SVnFjJVmEXfwB02WBTPYIJTj1LsPZx2WiKtvyMhVtGtkpOO1Oo6Grn6L1nqh1pGhjtCCNyOxKJroHMWl5MDvOFDOSd5oYMynCKXnDi9qOPh4POic73+p2aROrdaToayTvNNFXazBbGcJE9yC2nipktCCN2MlSxFf46vM0dPXjtDtNVq/lrRrKtrwsO/mhrGMcvlHHZI9QTJcFsvl4AWMlWbJjKli9krZeLS6hF+TIpKS5l0q/1/AdZ0GkiS2D5yW1a+4ZhsmSAF4/ep37ldfkiPKahzvdfVpcf39RdqS3G3uoCfwVQfpWREywZSDlExKLGpn6UgTGbv6s/uIqYzW35LRcrvtS+rQDLI7IRE/pjfWvv+FqbSf3gjez28CKcGMbtIn7SbnbwrQ1kRi5+rLq0xxG6+/IDjTHbTGDA1Lkpu/kg8WWU1yubKdx31ZCDa0JNbSm/+t9ZJS3MfOVKAxd1Dx3KJvRpnI5RZm1yJUhTTfLo75H38mHx7wTSC9rpXW/H2FGNuwxtKb3yG+5UtPB7PUxGDirWPpRFiNtdaTbPoFKKLi04EmGu1vx/OwKBs4q5myMI7mkmY6odwk3tmG3gRVd0TvJv9fNnI1xGDircPsgk+GuZjkyuuDgxHB7PWsP52HgrGL2+hhO3m6kJzaYSBNbgvStaDvgT1FTD49uikffyYen9mUw1NspC6E0KyUjzZVs/OoGhi5qzD3DCL1Q9rOQga2hMRmOzj9p/INk0PMnP+v86Z//xuszhRBef0YGPwghioQQHwshDP6lyWDCbDs5XVLVruHW2lWyox/uamZRSDp6Sm/mv3f2R6op130pQ/0aWcUrf5PKcE+77OTzvVbQpx3Acce36Cm9cQm9wEhbnbz5itZ7Utbai406CX0nH9YezmO0sUzOpZe+tZrbjT1YbDmFgbOKTQk3Gau5RfwUBwLGW1Ll/zpXajqYtiYSQxc12xJvM3TpKDFm9gTqSQSSXtbKFK9wjFx9CU69y0DKJ0RNtGO3gRX9X+8jsaiRyR6hmK0MIeJyBdoT77PbwIpQQ2sGkg/xZf49Ji0PxtwzjOirNfQe+a2sBocyjuCXVITpskCmrYkku6qD9kPvEDDekkgTW4ZzE2VlPvOVKG41dNO4byv+uhaSKi08z7q4a+g7+fDIhlhKW3op3vACvuMsSJ45j/sVV1n1aY6cr61u11CweuV/5de7W1l24LKMTVN3P3krnkUlFGQ4OjPUr+GZ8Esy4XRqtDI2WYtc6dMOyKT21L4MOSJQCQXFG16grVcrp/sWR2Qy2nBXdlhlW16mvrNPxua5Q9mMVd8kzlxS71X+r5Nd1cGjm+JlxT1Wmk2MmT2+4yy4F7yZ1NIWZq+PwcjVl5D0Ukbzz3LQVMKm5cPtnLzdyIy1BzFZEkDE5QqGs48TMcGWUENrOqLe5fCNOia6B2G6LJDP82rpiQ0mzMhGwubSUaJyqzFdFsik5cEkFNTTdsCfPYbWMjZhF3+QsUkuaZaxiZpox2jhedYezsPI1ZdHNsRyqaKNmsBf4TvOgtjJ9jI2Bs4qHvNOoLajj4ptr6LWeaDK7xXJ2FipEml+EEk8xGakuVLGxm77abr7tGS7PiMrf412AOe952VsBgYH5XTelaVLaOvVykJgcUQmQ5puGZsbns9R39knp54eYnN2zgLUOpIIqGjTMGdjHPpOPjI2STMc8R1nQX2ID0VNPTI2G7+6wcj1ZBKmzkWto/jZyOCCg9NPGkL6at/8Pxlv/5kDvyiEuPNXxuo/d/5C+u6Xv0UEs4QQ7UIIvT/7Ox0hhIEQ4qgQIuRfmgyMZtqgp/Rm9RdXuf9DDhETbDloasdwbiLPHcpGT+nNopB0xmoLOWhqR5C+FUMZR1j9xVW5MDvSXEnKrPkEjJeiiYjLFRg4q3h0UzydGi1pVkpUQkF9iA+f59Uy0T2IWeuiaejqJ99rBSohOZDrdV1M9ghl0vJgLle2U7zhBVRCQd6KZylp7mXOxjhMlwWSXNJMy4fb5ZSIdmAQxVsnMHbzJzj1Ll3ROwnSt5Jy/5punHanoe/kw7bE2wznnGSPoTXxUxy4X3lNTkks/SiL0cLzhBnZEGZkw2jheTZ+dUPekMPt9cRPcWDPA4W4LfE2+k4+2KiTGNJ0c2q6tJm6oncSf7MeYzd/FG+doLpdw3cuT6PWUVC+9RcklzRjuiyQKV7hlDT3yg68eMMLXK5sZ9qaSCZ7hHK8sIEq/9dltdjQ1c+sddEYufryeV4t9SE++OtakGalpFOjxcE/GQNnFRGXKxg4fYCA8ZZShNBcKRP26i+uMpRxRMZ4rLZQxnh51PcyxhETbP8qxskz5xEw3pKB0wcIzyzHwFmFg3+yjLG/roWMsZGrL7PXx/xNjKetieRyZTtF6z1ljO809TLFK/wvMM52fYY+7YCMcUh6KV3RO/EdZyFh3Ncji4ptibfp/3ofuw2s/jrGBWk/wnjpR1lypHi/4ipx5hLGwzkn/ybG3TFBBKfelTHWDgzKGLd8uJ2k4iZMlwUyZ2McJc29cgRbvOEFLlW0yRhfr+uSMc73WiFjPGl5sIyxSkgF806NFnu/MzLG2sT9BIy3lGoPLdVyanHt4TwGz8cSpG9F1EQ7xu4VyYJuedT3jNXc+guMH6ZtR5vKiTGzlzBOPsSmhJsyxt19Wq4sXfLzkIGBEem2T/yk8c9KEwkh/h8hxH/+nd8/K6Tbo/91ycBgujX6Tj4czKmiK3qnHP6XtvRitjKEORvjqO3oI81KSaCeJf1f72Nd3DVZlQxnHydgvCUJU+eiHRjEbvtpjN38yShvo9LvNVRCQU3gr0guacbI1RcH/2SG+jXEmTsQbmzDyPVkOf+/KeEmPbHBBIy3JMPRmfJWDbPXx2DuGUZVu4ZrHu6odRRoju4hPLNczmGP3Ssi3NiG+CkOjDaWYe93Bn0nHyKzK6kO2CjnmlNLWzBbGcIjG2IZGBwk3fYJAvUsGc45Kdv0+tHrDOecJFBPsmlgcJBHNsRi7OZPelmr7NiqAzYSmV0p2zTacPe/bMo7Ldu08asb9B75LWodKXyvaNMw2SOUyR6hVLRpyHB0Rq2joPfIb9mUcFOuuYzW3yHc2IY4cwdGG+7i4J+MkasvkdmV1AT+SnYg6WWtGLv588iGWLQDgyRMnSvZlH2c149eR0/pLRURH9iUbvsEgwMDPLIhFrOVIaSWtshpruqAjRzMqULfyQd7vzOMNpYRP0Wyaay2kGfCL6Hv5EPYxR/QHN2DWkdK81S1azD3DGPWumjKWyWbAsZb0hMbLNv01L4MRq4nEzHBljhzB4b6NTju+BYjV1+Sipuo2/UmKqGg0u81MsrbMHbzZ/b6GPq0AyRMnUvAeEuGso7JqaC1h/Po/3ofgXqWpFkpqevsY87GOMxWhlDS3CvbVOX/OlG51eg7+WC3/TSjTeVEmtgSZmTDWM0t5gWkoKf0JvRCGfUhPqh1JGLKruqQbSpr7eXSgicJGG9Jd0wQm48XyCJhNP8sQfpWxE62Z6hPSrs8tKnw1edRCQUV217lUkUbJksCsFEnodEOcGLaXEINrRm6dFQmpNVfXEV74n18x1mQqlhIfWcfU7zCmbQ8mJLmXjlq74h6l+irNRg4q3Dee56R5koiTWyJMbNnrPom8987i6GLmpD0Uhr2vi1H8ldqOpjiFc7MV6Lo7tNyacGT+OtaMHg+li0nb6Gn9MbzsyuMFqQRpG8ldYNpunnMOwEjV1/CLv7w85CBvhFpVsqfNP5BMtj/ZwXkiL/z2jwhhPuf/d1DItERQhwU0jdJ/uuSgf5UK4zd/LnV0M0Nz+fw17WQowJ9Jx/ib9ZTHbARlZAc1o4zxegpvfFLKqL3yG/lDRd/sx59Jx8pXZSbiL+uBem2T3C7sUdWUKONZewxtCbO3IEOjRZzzzCmvhRBn3aAGDN7woxsGGmrw3HHt5guC6S0pZcrS5cQMN6S0cLzLI7IxMBZRVJxE+Vbf4FKKND+4UP5QQ5JL6Uj6l05DfV5Xq2c2x/KOILvOAsylS5cqenAyNUX619/w1jNLXYbWHFi2lwauvoxWxnCzFeiGOrXcGq6IxETbBnuacdu+2kmugdR3a4ha5ErgXqW3K+4ylP7MjB0UZNe1krxhhdQ6yhoO+Avk8vGr27Q9L5aTn1FXK5AT+nNM+GXGEj5RFa+6WWtGLqocfBP5v4POYQb25A8cx5V7RqmrYlk9voYhrtbiZ/iQNREOwa1/VipEjFbGUJTdz8Zjs7sNrBirLZQbiU9fKOOW2tXyWrW87Mr6Cm92ZZ4m3vBm2XnG5x6Fz2lN8sOXEZ74n1ZqScWNWKyJID5751ltCCNUENrUhULKWmWFPyjm+IZaa0hdrI9MWb2aLQDPOadgLlnmBwthBpaM5p/loU7z2Hs5s/J241yy2P/1/t49uPv0HfyYdfZe+1PWgAAIABJREFUElkl1+16E7+kIvSdfFj1aQ49scH4jpMKrF/m38PI1Ren3WkM55xkt4EVFxycyL/XzWSPUCy2nGK04S5RE+2In+LAcFczj2yIZdqaSCraNCTPnCeRW2k2Dv7JmC4LJLW0hRy3xbIidvtAes7CM8sp2/IyKqGgcd9WmQTWHs6j/dA7qHUktR+VW42hixqX0AuyKs9UunC5sp1Jy4OlyKK3k0gTW6nrrq+Hma9EMfOVKOo7+zgxbS6RJraMVd/ERp0kR8aZSheC9K0YPB8r12WicqvliLn90DusPZyHntKbTQk3ady3VU7jPYzc3D7IZCD5EAHjLclxW0xZay+mywJx8E+WWk0fPGfagUGmrYnkkQ2xtPVq5edstOEuFltOYezmT0JB/c9CBjb6RqQqFv6k8Q+SwRQhxCUhtZZeFNJ3vwghhLMQIvZPXmchpC8CG/dn/z5TCFH8IO10TAhh8i9NBuMnP8qMtQcZbSwjzMiGbNdnZCXll1RE4avPo9ZRMFp4nse8E5jsEcqQppvdBlakWSlJKm6SVc3DSGAo4wj2fmcwXRZIp0ZLuLENJ6bN5XpdF/pOPiyOyKTlw+2ohIK+Y6E47U5j0vJgqto1RE+yJ3qSPVXtGrkVse9YKCohheCLIzIxdvPnel0XJ6bNJX6KA50aLabLAnkm/BJDGUdkJ7f6i6sYuqhJKm4izUopFUU1ktNYuPMcY8UXUesoaNj7tux8Lle2k+36jJRGaCxjxtqD2KiTGK2/I3e8RGZXoqf05nhhAwWrVxKkb8VwVzOPbopnzsY4hrtbCdK3omD1So4XNqCn9CbicgXlW3+Bv64FY/eKsFEnMW1NJKNN5YQZ2fCdy9NEX62RUx2N+7bK6/540DnMVoYwpOkmaqIdaVZKkkuaMXRRs/qLq7ITHTwfi4N/MiZLAujQaEmYOpcT0+aSf68bkyUBLI7IpHW/HyohRVdOu9MwdFFT0aYhxkxa9+p2jXx+ov/rfaiEgqb31Sz9KAt9Jx+u1nZyaroj4cY2dPdJ627vd+ZH6/6wcJlY1Ei67RPsNrBiqLeTyR6hPOadIK974avPy+IiKreabNdnJPJ4sO4zX4mS1z3HbTEHc6rQU3rz3rd3uLV2FUH6VoyVZP3Xuve0E6RvRb7XCk7ebpSjooptr+Kva8FQ1jHstp9m2ppIevoH5HW/UtOBvpMPW08V/te6F6TJ617b0UfURDtSFQspb9XIdZCHYmgwLQa3DzL/5rq7fZDJYFqMvO5eMbkYuqgpb9WQqlhI9CR7ajv6MFsZgvI3qWhPvI9a58frfqWmg+9cnibc2Iae/gGmrYnEbvtphi4dxV9Xam54KEBO3pbWPUjfiuGediZ7hPLopnjGSrII1LPk1tpV8rofzKmSW21HG+4y85UoyR803P2LdX/uUPbPQwZ6hqTMmv+Txs/xfv/M8T/+Af6RoTtxNta//oaB0wckpZ24H+tff4O5Zxhj94pQ60gbfOlHWUxaHkxbr5Y9htaUvOFFSHop+k4+1Hb0ET/FgXTbJ8iu6pAdZY7bYg6a2lHU1IORqy+bEm7Kh2aG+nowWRKA0+40Wj7cju84C0YLzzPzlShpY+ecRCUUdEXvZP57Z5n5ShTDXc3461rQuG+r3Dk0qO0nYoIt1zzcZeV2u7GHlFnzOTXdkcSiRjlldMHBSWph7ezDwFnF8qjvufvmi+w2sGKkpRqzlSG4fZApp0CGMo7wmHcCVqpE7ldcldfnqX0ZmHuGMVp/h0A9Syq2vSr33Lf1aokxsydT6ULohTL0lN6klraQMHUuMWb2XKmR1mfHmWJ5fTTaAblYV7frTQLGW3K/8hpTX4rAaXea7EhGC9Kw9zvDIxtiGc5NRK0jpQwW7jzHRPcgeX0KX32etYfz0HfyobxVQ8QEW87OWcAX16RI6fCNOs7OWcCp6Y6Utfai7+RDxOUKbq1dRaihNfWdfZguC2R51Pe0H3oH33EWjDRX8siGWOz9zsjkPHg+FsVbJ5i2JlJen3vBm3lqXwaTPULp0w4QpG/FlaVL8EsqwtBFzdXaTnl9Uu62oKf0JuVuC5lKF2LM7Lla24mhixq/pCL5zEKfdoDJHqE8tS9DjmjuV1xl2ppIFG+dYPB8rCwsHq7PSHMlvuMsKH1rtXxIsL6zj1BDa26tXUXE5Qr0nXwoa+3l1HRHzs5ZwOEbdegpvfniWi1n5ywgYoIt5a0ajN38WXs4j8JXn5ci565mJroHsXDnOTqi3kWto2A4N1Fen9GCNJkknHanMfWlCIY03QSMt6Ru15ts/OoGRq6+aLQDHDS1I8dtseycr9R0EGNmT8LUuaSWtsgprEylC3sMrWnt6cfQRSKJim2vEqhnyWj9Hcw9w3hqXwbaxP3y+lipEnnMO4GhjCNyetXtg0zMVoYw0lLNbgMr7r75otymW9/ZR5iRDRccnIjMrkTfyYfEokZOTXeU2sP7tD+Lc7Yeb0jyzHk/afybDP6JY5zJDJ47lE2O22IiJtiScrcFfScfSpp7CdK3ov3QOzjtTkPx1gk5NB4YHETfyYfg1Lucmu5IjttiglPvYrIkgMGBAWlzZB/nMe8EnHanUfKGF0H6VpQ098qbP2KCLZV+r/HcoWymrYlkIOUTyeE1lWPuGcbaw3lcWvCkFO7frMfAWUVtRx8B4y3pOxbKvIAUbNRJ1If44DvOgg6NVlY6ceYOpCoWsuXkLSYtD2a4uxWVUDBWfJHZ62N4JvwS+V4rCDW0lp3z1dpO+UDVM+GXmL0+htHC86iEguHuViYtD2brqUJSFQuJM3fgYE4VRq6+dGi0+I6zYCDlE2zUScwLSKHvWCgB4y2p7ZBIJ/5mPVET7bi04EnWHs6TiKSpXPpM1dJJ7T/HQE/pLWNQ8oaXjMFw9nEZA5MlATIGSTMc/wKD9kPv8Jh3AotC0mk/9I6Mgb6Tj4zBlaVLeO5QNjPWHmSs+iYqoWCkuVLGoOQNL6In2RN/sx49pTd1nRIGlX6vyRgMpHwiY2Dk6vsXGBg4q2QM+o6FyhjUh/j8VQzyvVbwTPglJi0PljEYK74oY5DvtULGQE/pLWPQsPftv8BguLtVxiBVsVDGwMjVV8ZgIOUTGYNKv9f+AoOoiXYyBuaeYTIGY9U3ZQxy3BbLGOg7+fwFBg/3T5C+lbx/UktbODXdkStLl/Dsx99hsiSA+1U3pGcu56SMQfGGF4ieZM+dpl4Zg4f7Z15ACjPWHmQg+ZBM2g8xuLTgyR9j0NUs75/Z62Pk/RNqaE1br1bGIM7c4a9i0Hcs9Gcjg6QZjj9p/JsM/olDx3gaIemlRE+yJ2/Fsyh/k8q8gBSa3lcTaWIrdwaN3SuSnbW+kw+3GrqJmGBL2wGpPc9573ly3BYTO9meHWeKmeIVzlhJFr7jLKhq16Cn9KaqXYPvOAv6joUyxSuc9769Q4ajMzlui3HeK0UFbQf88de14FZDN/pOPrT1aiVncK+IScuDibhcQaSJLQWrVzIvIAXlb1K5F7yZ6En2hKSXYuii5n7lNWlDtdfLdY+HB79s1Em4/v4iWYtciZ/iIF+xMZp/Fn9dC5mwGrr6pY17+gBmK0N47lA2aVZKrnm4s/qLq8xeH0Pjvq0E6lmSXdWBgbMKjXZASpNlHZNbHpNmOFK84QXs/c6geOsElX6vETvZnqTiJoxcfRkrvij9m95O9JTeJBY1ssfQmkq/17DYcoqlH2VRvOEFTk135PWj1zFdFigX7TXaAdkZBupZMpgWw6TlwXjF5JIyaz6pioUsj/qeRzfFM3D6AEH6VqSXtaKn9GZQ2y85/utSWimhoJ6EqXMp2/Iy1r/+Butff0PpW6sJNbQm/mY9JksCGO5slBTvwIBcJwnSt6Ju15s8uime5VHfU7B6JSmz5uMVk8uk5cFyVPOQrPPvSQp5OPs4M1+J4vWj1zk13ZEMR2eWfpSFxZZT9H+9jz2G1lQ/eG6Ge9rxHWfBWEkWRq6+7DhTTOxke6r8X8d573ns/c5QtN6TcGMbPs+rxXRZICPNldJzU1uIgbOKKzUdHDS1o3HfVmavj+HxoHNc83CX6hoXypjsEcrA6QNS2rCrXyaCh/WWaWsi2XLylnwy1vX3F5n6UoR8ILK0RSLZ4fZ61DoK7ldek4u4f7q35r939v/zvWXuGfajvbUoJP0n7a3I7Mq/2Ft1u94kepI9GeVtP9uhM6vxhpya7viTxr/J4J9KBlO5VNGG7zipq8DI1Zc7TZIiHS2QWjKTS6T0w1htoZyvfxgaGzirqGrXSKmNiquYLguU5xvKOPLj+QrP/6T5EgrqfzTf5cp2eT5DF7WsmB/Ol3K3Rc7FG7v5k39Pqmk8nO94YYOcejFZEvA35xsrvvij+QZSPvnRfCN5p//+fJeOYuii5otrtQTqWTJWfJGJ7kF/Md+thr8/X3ZVh9xpYuii5vCNuh/Nl1r69+c7ebuRgPGWaP/w4d+fryRLns93nAWD56TWVXm+68mYrQz52/NlHftJ80XlVsvzTVoeTGLRj+e7UtPBnj+Z78v8ewTqWaI5ugfTZYGkl7USZmTzd+fz17VAm7j/R/MNZx9nskfo354vLUaeL0jfitH8s391vvDM8h/NF3+z/kfzZZS3/Wi+6Ks1P5ovqbiJiAm2aBP3Y+zm/zfn6zsW+nfn64re+aP5Bk4f+PF8OSf//nznY5niFf7fzhdxueJH8yUU1HPQ1I6yLS//bGRwYtrcnzT+TQb/TDIwmiIXPBMK6qVTk7ZPULTeE7OVIZS3aqSOoXYNk5YHS8fTFQtRvHWCtYfzJKfbKRXrxkqz8de1YMeZYhbuPMd3Lk/TuG8rRq6+XK3tlA6zlbZgsiSAKv/Xuebhjr3fGcIzyyXld68IPaU3ow13CTW05tmPv8NGnUTSDEcGkg9huiyQ1p5+fMdZkH+vm6kvRXDNw52sRa7MWhdNammL1BEyOCgpypyTxE62Z12c1Gt+0FS6OsHAWSWr3C/z76F46wTFG16geMMLTFoezJf596TIorMRA2cVPbHBHDS146l9GayLu0bsZHuGc05KCntggIDxlmxLvM2sddFkLXLlmoc7U1+KIP9eN77jLGjt6cd0WSADyYdImuGIjTqJZz/+TiqWNkidPGP3iqRDU5nl2PudkVo2/V/HZEkAqaUtqISCq7WdGLn60rhvK9+5PM3CnefYcaZYIsJS6bzAUNYxdhtYsfZwHoq3TpCqWIjm6B65QK8SCspbNZitDKFovSfptk/wmHcCCQX1UsHxQVplrPgiB03t2JZ4m8eDzhFjZk/Lh9sxdFEz1C/Nk1zSzOz1MVT6vcYNz+eY4hVOZHalVPzsltS1NnE/YUY2LDtwmeVR33NquiOD56J/lN55eN1HrvtSagJ/xcxXosgob8Nf14K+B/WUoaxjxJk7MP+9s7j+/iKRJrbcr5DOQYy01bHbwIovrtViseUUZ+cs4O6bL8oHzlRCQWlLLwbOKrqid3LBwQnnvdI5kkA9S0bypFPpowVpBIy3ZMvJWzjtTiNT6ULbAX+meIVzu7FHKqZ3S1iWb/0FyTPnYf3rbziYUyVh+UDZj9XcItzYhsURmTj4Swe2agJ/hbGbP919khrPrpIOTNaH+JDjtpgZaw+y62wJ/roW9PRLEd9QxhHipzjgFZOL2weZnJ2zgL5joXKdTq2jIPpqDZM9Qrm1dhVlW15mzsY4jhc2yNGmgbOK9kPvEDXRjkUh6TjtTiN6kj2j+VL76ZCmm0A9SxKLGnlkQywZjs4UrF6JuWcYd5p6UesoqO/sk+36OZyzha4h8VMcftL4Nxn8M8nAeCrD2cdJs1LiuONbTt6WnGT+Pam/OM7cgb5jofLGjjGzx2LLKbKrOqTrJO62YLf9NBmOzgyej8XAWcX9H3I4MW0uznvPczCnSuphb9cw85UoUmbNp+XD7bKzjphgi+OObzle2IDvOCmEnegeRK77UjRH98i53ehJ9iw7cJkdZ4pRCQXdfVome4RyacGTVPq9Jr1vxVV2G1jhvPc8yw5clvP2Rq6+FK33pOl9NRPdgxjSdBNubMO6uGusPZyH7zgLhnvaMV0WSN6KZyl89XmM3fypfhDxPPvxdywKSWePoTU9/QMYOKuoDthIxbZX5TbK3QZWvPftHZZ+lEWMmT2jDXcxcvWl71goV5YuYaJ7EPn3ulHrKHj96HUc/KWe++FuKW3Tut+PW2tXMfOVKNlpR+VW47Q7jVPTHRkrkbpKhjKOcMHBiSle4SSXNKMSCnadLUHx1gnpKov6OxJWJ94ndrI9irdOcL2uC5VQkFgk3fKZbvsEQ5eOSiRUfJGkGY7MWhdN9NUa6U6eqzXMWhdN0gxH2g+9IzmmS0eJNLHFwT+ZpOIm1DoSOSneOkGO22K0J96XHGr9Hfl9d50tQSUUtPVqmeIVzgUHJ2oCfyU5lpIs9hha47Q7jajcagLGW1LRpmHWumhurV1F2wF/KSXW3UrEBFtWfyFdFaLWUTDU18NE9yCuLF1CyRte0jPScJdAPUuWfpTFe9/eYbeBFZ0aLQbOKiq2vUp1wEYme4Si0Q6wx9CabYm3efbj7wgYb8lIcyXGbv4Uvvo8eSueld73QWpq7eE85gWkSCebNd3oKb1pel9N8YYXmLH2oFzHirhcgUvoBXYbWHG/4ioGzioq/V7j0oInmewRSnqZVDPxSyrCSpUoOeSmcvSU3miO7iHXfSmPbornVoMkII4XNjAvIEV6Rh4Ij9H8s6TMmi9f+6ISCiKzpcL+iWlzuf+DdGJ9MC2GDEdn7LZLF+E9JB+LLaekQ5qJ+yXsa24RZ+7AY94JhF4oQyUUnLwtXfuRZqVkOPs4+k4+clvx5uMFPxsZxJk7/KTxbzL4Z5LBhOnU7XqT9kPvYOCsYvBcNIWvPo/ZyhBG6++QqljIo5viaevVctDUDue958kobyNgvCWbjxfIG/54YQOLIzIJM7Khql2DjTqJU9MdGe5sZIpXOHkrnmU4NxEjV1/qQ3xo3e8nPbjnYylYvVJ66JrKOTtnAYq3TtDc00+kiS0uoRdkxb/1VKFMBolFjbh9kIlaR0FdZx9WqkT2GFoz0laHuWcY1zzcGc4+jqGLmsZ9W2l6Xy07tXyvFZKibKkmZdZ8Ji0PpqFL6kp6eBeMv64FfklFbEu8Latg199flAqVXf1YbDlFqKE1Iy3VUig9da6chmn5cDsNe9+WCC/7ONc83DFwVjHSVkfSDEcmLQ+mtqOPcGMbHt0UL6clFoWks/VUoRxBuYRewHecBc09/SjeOiG3u5qtDJEU9gPyzXVfSn2ID3pKb+4FbybXfakc/Zya7ojZyhCq2jWEGdnwmHeCTLwuoRfYfLwAlVCw5eQtnPeex3ecBSdvN/LopnjCjW0Yrb+D2coQKTpL+QQDZxXXPNyp2/WmVFvZ+zZXli6RVOaDW2wne4Qy0lxJqKE1FltO8WX+PXzHWeD6+4ts/EoqkG5LvI3T7jT8dS1IKm5izsY4IibYUt/Zx6TlwSTPnMfA6QPoO/lww/M5qgM2yo442/UZ6f2yjhE/xQFzzzBGWmvYY2iNlSqRuk5JNT8Tfol1cVL9aMeZYh4POoe/rgWppS08siGWSBNbmnv6megexNk5C2RlX7B6JYPnY2WS/s7laYxcfRnOTSTO3IEpXuEMdzay28AK619/Q3W7BrWOgsURmbIq33W2hM3HC6QDlOVtzF4fw0FTO9p6pXbcVMVCRhukYnPhq8/L0VL7oXeo2/UmRq6+jFxP5srSJUx9KYLh7lZ2G1hht/00FW3S+y07cJn4m1LkE5JeKkc6lyvbJZKdaEenRsucjXGk2z4hR97FG15gIPkQekpvuqJ3Uh2wEWM3f0YL0shxW8y0NZEM9XaSMHUu9n5nKG3pRSUUfJ5X+7M4Z4WuAbGT7X/S+DcZ/BPHeLM5JEydS1GTdKRfJRQs/SiLqNxqQg2tMVsZwnB7PfleK9BTelMT+CvGSrKYvT6GGDN7LlW0yQ7MJfQCxwsbiJhgi+myQMbuFXH3zRfRd/Kh9K3VjN0rQvHWCSIm2HLydqOsRua/d5ahvh6iJ9lLRdWSLGoCf4Whi1q+gdTe7wyhhtZE5VbzxbVaVEKBjTqJkZZqEqbOlWoX/q/Tut8PYzd/6eIxbT+PB50jSN+K4NS7JBU34TvOQup1f3Bfy8PNoTm6h4nuQYQb29ChkS5O89e1YPPxAq7UdOCva8GsddGMFqSR4eiMntKbax7uDKR8wmSPUPYYWlP14PsL1DoKPD+7QmmLVIuY4hXO0KWjXFm6BD2lt3TzZG4i09ZEEqhnye3GHtbFXcN3nAWLIzJp6Oon1NCaScuD0f7hQ26tXYWe0ps0KyX3f8jhkQ2x+I6zIKO8Db+kIgLGW7IoJB2NdoBMpQsmSwLoit5J2ZaX0XfyIWmGo3x4SK2jIKFAutd+t4EV8wJSGOrtJG/Fsxi5+tKw9205hRBnLh3acvBPRiUUHMyp4vCNOsKMbLD+9TeMNFdStN5TVt/3q25g7CZ9T0LK3RaUv0lFJRS89+0dUu62cNDUjkc3xXO/6gYV215FT+lN0XpPRpqlS/nCjGw4fKOOZ8IvoRIKNn51g+t1XcSZO0hnDvLPyiSbt+JZhno7MfcMky6jyyxnedT3qHUUWGw5RXmrhqQZjph7hjF4Ppau6J3oKb3JVLqg0Q4wY+1BAsZb4pdUxMavbuA7zoJHNsTS3NNPuu0TcnSl/cOHUnuwYiENXf3M2RiH7zgL1sVdY8eZYgL1LJm2JlK+kuKhQx+6dJQpXuGcmu5IaUsvViqpFfi5Q9lEZleyx9CayR6hDHe3cs3DXSby0cLzzFoXTexkqQ35IVm7/v4i8TelQ2IT3YMYbSyjeMMLUvvw1l8wVnOLx7wTiDSxJam4ieDUu6iEgseDzjGo7Sdqoh3Gbv7cr5DOpRg4q7i1dhUjrTXYqJMIM7Lhi2u1ROVWoxIK7P3OyFewGLqoGck7/Q9fKf1wPKZrQIyZ/U8a/yaDf+LQnyrdo7/swGUuV7bLG+Gahzu1HX3sOluCWkfBFK9wWj7cjuboHma+EiVv1k6NluINL6Cn9CZphiPagUHWHs5jj6E1s9fHoD3xPmOl2Uz2CMV3nAVOu9Mob9VwZekSDJxVXFrwJNfruvg8r5aA8ZZSv3xuotzDHqgnpWmGe9pJs1Kip/Qmxsyeus4+dpwpJtLElile4bTu92O0qVz+bJsSbtKh0VK03hM9pTfJM+eRWNSIV0wuuw2sMHL1RZu4n7Hiizju+BbfcZIyH62/Q7brMxg4q8hUunCroZuo3Gr8dS2Y6B5Elf/rDKbF8Jh3Aiohbe4hTbesXGMn29PQ1Y9fUhERE2yZ+lIE7YfekQ9SqYQCB/9kWnv6KXz1eYxcfUmZNZ/kkmaSS5rlzzZw+gCjhedx8E/Gd5wFznvPM9pwl+9cnpbOBUyw5XZjDwdzqoidbM+k5cFUB2xkSNPNo5viUQkFy6O+Z6ivhyr/19FTehNn7kBUbjXbEm8TbmyDvpMPHVHvMlp/B+e9Uvug445vGSu+yK21qzBy9eXsnAWk3G0hqbiJIH3ptHrRek9G889i73cGlVBI1zI0ldO6308602FiK2GTXUmMmfTZagJ/xXBPu/zZFG+dYFDbT6Xfa0x0DyLO3IHP82rJvyel8B7m98dqC1kUko5aR8G8gBTGSrLI91qBkasvewytSS9r5eTtRpJmOGLs5k/xhhfo1Gix235adqIjzZW0fLhd/mzvfXuH8Ewp9ain9KZu15vyxX8qIZHJUMYRyrf+AtNlgcRPceCLa7Vcre0kzEj6bFeWLmGs5hbK36TiO86C+e+d5f4POfR/vQ9DFzV7DK1ZF3eN44UNnJruiMmSAEre8KKnf0AWXbPWRTPSWkPT+9KtoAdN7QhOvSsfvtRTeks3kHY2svSjLFRCgZUqkaGsY5RteRnTZYEEjLfk8I06rtR0cMHBCQNnFXkrnqWqXcPjQedQ6ygw9wzjfsVV+o5J348RamjN60evE3+znhPT5qKn9Obumy+i0Q7IkdTs9TH0f72Phr1vM9kjlKiJdpS3an42Mnh4uPS/G/8mg3/iMJ4lfYPVw+LwwOCgXBDTU3oTMcGW+Jv1tPVqad3vh8mSAFRCIR0Cy01ktP4OG7+6gb+uBQbOKsq2vMztxh6u13WRMHUu+k4+ckpptKmc4ezjslM0XRZI+6F36NRoOXyjjnBjG1m9RV+tQaMdINv1GSa6B8mRx1DGEUZaqrH3O0OgniX6Tj4UrffkSk0HJc29JM1wxMBZRZy5A2sP5zFWc4tKv9eYvT4GtY7khJreV9PU3c+yA5c5aCrd2ppu+wThmeUMavvJdV+K2coQ9hhao/xNKgPJh9CeeJ/570n30Jh7hsl3HQWn3uXsnAUYuqiJnmTPqk9zqG7XULfrTeZsjEOto2DOxjjqdr3J/R9yWPVpDtGT7DF0UXN2zgJ2nS2R7zwy9wwjSF9S68NdzQykfCKr60nLg8l1XyodsrtcwQUHJ+lchakdyw5cpqm7n6b31bL6n7UumtH8s4zV3GJd3DUZn6QZjtxp6uVKTYesLAPGW2Lvd4aRlmqGLh3FJfSCjE93TBAa7QDRV2sIM5LwCTeWVGR3n5aOqHcxXRaISiiYsfYgw9nHGW0ql1Mk+k4+lLzhxfW6Lm439nBquoSPv64Frx+9zmj9HUbyTsskbrIkgNb9frT29LM4IpNIE1v0lN5kODoTmV3JwOAgV5YuYdLyYFRCwaKQdAbTYhhur8dxx7cE6lmip/Tm1tpVXKpoY8eZYlJmzcfQRU2MmT1eMbncr7xGdcBGHtkQi1pHwWPeCdSH+FDX2cdzh7KJmmgnR2KhF8pILmnmhudzTPYIZbeBFQt3nkObuB9t4n4eDzrHbgMrzFaGcM3DneSSZkIvlJFmpZT0zSqRAAAgAElEQVQ6lSba8dyhbOoffAnOY94JqHUUPLIhluqAjdyvuoFXTC6xk6VnInnmPHacKeZyZTuFrz7PFK9wAvUscfBPZqStjsG0GBaFpKMSCia6B0n1moFB+VzBwz37f9u78rCqqvX9LmQSEQRBcEBmBBxCJNJwwjG85GwOVzMrwyi9KIqRZnrzp0XhxbhRljkVqYkazvOAqDijgCCToAwyzzP4/v7Y5+yLpWVlqbXf51nP2Wefb52997e/tb41fIPHh8fkNmvjE/GzbXaLqROvZJXw4q0SeSbvr2kted5nJ8lt1q+F1SPpnC00dBhq4PBQRVEGf2Ixs3NmuIkTTUYG0QeW1HaVomF29d/NC16DpKQYuVIjvF0khep9fuVRHu3+LFv2nk1fYUktlxkM1rdnxylreeejt/lFTAbrY3ayMTGKW69ms2RNIC1f28Iwwy5sM3QJfWBJHTcfBmrbsNeSA4ybMkJOQ1hXnMvp4Zd4bZIXXd7dxwAta+q4+dAHljQavkzewC5b9x4jrmWz6UY066K3MuysNMLqOGUtg1pJHYivsGTL3rN5zMWdHh8eY+qcicwqrmRFVTUbspPo9flpnhvuSee5u+jXwopaLjPoA0uajAziFlMnvrMngdWRq3kitYCnbxayevd/ueRAIre1c6bp6GBZYfq1sKJ+P3+e8ezPwaGn2HA7nuVV1cwtlTKy9Q06Jq87q/kV1MqeHSat4Z5O3RkSnca6qM2MzyljZEIuK75dRhufCK416kKj4cvk9xKgZc1nAvcydsILnPrNBdYVZUvZxfLLef2VF+m29CAP2PeU+WU4eBHDDLuw84xwFoctZPjl22yMO8KGi3v49YVM5q/yY6ep6xnS2kFWuLruvlysY8PnPjjM5FnjmFFYwZqqStbfSefYr2N40XsIuy3YI3f0PpBmjZvaOtLOdwertqzggaQ8Nt68wtrD67jyWDIjzbvSfHyorExma1jJS3kD/3OStxa9ysLyKhaWV7Hx5hUO/M9JRnv0pZ6HH2drSO9leUs7Kdy0eVdJaR9cy4u3SnggKY9V33/EuT/EcVNbaT1/sY6N3KF1W7CHF72HcOzXMay/k86aqkpmFFYwedY4PvfBYR52dqOuu6/8XkJaO8hr7F9fyJQUatwRhl++zeKwhew8I5xhhl1kZaSWY7elB3n9lRc5a1ssa8tLWFeUzficMsZOeIHPBO6VBy5qOV5r1IU2PhEs3/g+IxNy2ZR6jnVRmxkSncaspW+ww6Q1DGplT/1+/rIcH+/Vm32DjjHdfypzSytZXlXNhtvxHPZZNGOGDKSjX+Q9cmw6Opjb2jnTee4uVu/+L0/fLGRUWiGrI1dz0b7r3GIqtfv3m8lx6wEBPDfck16fn2bOCl9WVFUzq7iSaX6TaTo6+JF0zp00dBisb/9QRVEGf2Kx69pDDlbWvHE6z90lK4M9nbrLnZDF9E08YN+TbksPyh3zMRd3uUNo2Xs2l+na0nR0MGdrSJ2keklCvQykbkTqUZy6E/KBpSyU6oaj389f7hCW6drKHap6rVg9Im8zdAkDtKzlzu2ZwL3c3b4b278UJnf429o5yx2Nne8Ops6ZyOdXHuUxF3fO2hYrd6jFYQul6KVnIvhFTAbvfPS2lD/58DoGnUhhQ9Z1KUtV5GrmlVayKe0Cd1+/w5rqamYWVTAht4x1xbmsKy1gw+X9TCsoZ21lOXfE5bAp5SxzSipZFfExDyfnszEjlsuP3GDN/jU8n1nM7A9mMfRMOi+PGioHRHNdvF9WmIed3eSO2vbN7Tzp3of9Pzkub/ad8ewvd5qtBwTIStpf05qGgxcx1MBB7sCMhi+TOxp1h6DuqNXvQs179TtSd5qB2jb3KB21clDvF/m1sJIVQJc5P8gDjpPufWRFp+6MO88IZ9yUEXRdvJ/7LHvISkfdMQ4OPcWavWFcdiiJ6f5TeTg5X1YATTeiGXEtm2Xr3mNaQTkbLu+X0mAW5zI+p4yZRRWsrqlhfW4qG9MvMb+sSsofkFbIhqzrrKiqZu3RjbyaXcr6/Ex5IJOUV8bisIXcHJvFxNdGyaGx1YOK+7WXc8M9f9Je1IqlzdAlcntZrGPzm9qL+p38+PuvaS+Wr23hmjZSezlg3/Oh28s7exLYlHbhkXTOHTV0GNTK/qGKogz+xNLDpScbM2LZlHqOmUUVLAydz24L9jBmyEB5tqDj5kNfIS0DrNSzo8u7+1i27j3O2XFNqptylplFFSxYPY9fxGQwZshAtvVeybVGXeS66uUFl3f38YB9T6nurWs8m1HEjEKpbtjZmzzj2Z8DVp2Q6/prWssjaQPPQB5ydOXcH+Lk2PjpBeWsKy1g6Jl0nu7fj/0/Oc61RtJoWF1XPaU+5OhKq5nbmO4/lRdvlbD2+Ldy3ZqDa2WzUHVd9fKHgWcgA7Ss5bqDVkexLmoz60ryWFtZzpqDa9k36BiTZ42jxfRN3G/jItfVcpnBAC1r2vhIaSkHh55iXfRWfn0hkzVVlazZv4YnUgvkuvsse1C/n788ig7Qsqauu69c94LXIG64eIs1VZXMKank0ZR8Js0cw5lbr/ykrq+QOvBwEyeajg7mRe8h3HTpNkvXLmJuaSUPJ+ezKfUcX918mXs6dWe3BXvk2U7zzt9sbAgveg+R33tuaaUsM69uvszC0Pns6r+by3Rt2dZ75U/q/kRmbl1jU8pZ+b139d/NM5795brNZSaolT1dF++X33vD7XiezShiekE581f5yTJzP3n7NTKzps395e2ws9s9MqN+7yHRafJ7by4zzeVNXfdmwMuSzERvZV1xriQz+9fcIzPq2aVauapl5mbAyz+Vmb1h8ntvLjMPlLczEZLMVFf/RGb2dOp+j8wEatvIMjPss+h7ZOatiKuPTBms1LN7qKIogz+xuDracubWK/Io/Wj3Z5lXWslrOaXcYeZMbVcpdklwVCq3Xs1mylsT5PXXXksOsKZKyrDVYdIaztawYlvvlaze/V9WbVnBgf85SV9hSYvpm6RE76UF7LFwL0MNHNiy92zeDHiZu6/f4bJDSfLIdE+n7hwceoqZRRU8YN+TRsOXMVBbsuFvjDvCrKVvyCNX57m7WJ+fycb0S7Saue2evY+kvDKO/TpGnjZHe/RlRVU1o9IKuamto5QkpJU9N1y8xS9iMpgwzZvarq8z3MSJz688ypKKKtYe3SivlZqNDWHt8W9ZsiaQvf/viLyZlzDNW97DUE/rN7V15OmbhXwr4iqjej8vbwS+tP4cE++UMdK8Kw0HL+L7urZceSyZjemXmOY3WZ4duby7j9kfzGJj3BF2nLJW3qeo3rmKt1Xr2uoO74LXINaWl3D39TvyXkSYoRTuorasSLYC8xWWHPifk8wqrmR15Goaey1ngJY1O05Zy4bL+5mzQooQq97gTp0zkY0ZsQw6kcJlurY08AyU9xsmbzwvW1SddO/DWdti5T0i9cj2i5gM1uem8vorL9L2ze3yRnNh6HzWRW+l+fhQ+mtKlji1h9exSJXIXb0kFjdlBMPO3uSmS7cZrG8v7wOdSC1gVXWNbJk1W8OKo746y+S8ctm57n1dW1q+tkVaeinKZlf/3fSBJfU8/Hh7yetsTIziO3sS5NnRfhsXpheUc9hn0dxn2UO2Vlq07zoPJOXx1qJXpXg/rR3YbcEe1pXk8crYYfL6/4BVJ1j53Qes2RvGtt4rOVvDih0mrWH9+UjWVEtZy9SKKnnWOG6OzWJIdBqXt5RmANvaOfNqdinzy6p4zMWdWi4z5H22K1klLFg9Tx5c2PnuYEPWdTZkJ8kb0a0HBDB/lR/rY3Zy5tYr9Ne0psnIILkt9//kOLe1k9ry8pZ2DIlO4+bYLKa8NYE6bj5ca6Rqy9VSKll1W/b6/DRr9oax8rsPOGDViUcWK6iD0JETDP1SUZTBn1h6dDCl+fhQhhl24axtsZyz45q8GVhbWc61Rl1ks7e8j+eww6Q1UhL0ymrZx2DqNxdYW1bETW0dqeUyg+n+U1mwep5sy11YXsUlBxLpA0uO/TqGdcW5DDdxorHXcqa8NYElawLl0VhuaSVXHpMCiHmvOcP6/Exua+dMo+HLmDRzDMs3vi+vIWeqUi+qLWdCz6Qz0ryrlOBkmjertqyQlVlaQbls3jdg1Qk5MqWBZyAbb12TbdpjJ7wgp+NU245vunRbHu03pl9izd4w6rj58KL3EMbnSDmRF+vY8PmVR7n1ajYPObpKQcdSpMxiatv8K1kl7Oq/m4HaNnRfdkj2Z9Dz8GNjwnEec3Gnrrsvz3j259mMIvZYKK0191pygAeS8ujXwkpysLq8X96DiOr9POvPR9Ll3X3017Tm0ZR8nkgtoL+mtWwSeLp/P+p5+PF4r95siD3IXksOyPb25zOLGaAlra3XHv+WMUMGylZeTTei+dwHhzlbw4oR17J5NbuUgdo2dPSLZM3BtbzgNYg6bj485OjKprQLbD0ggLM1rBh++TY9Pjwm28VX7/4vr4wdRm3X1+Vk7QaegfQVlvz6Qib7fyI5oNm+uZ0p+eW8NsmL2q6vS7b/WddpOHiR7G07aHUUl7e0o9XMbcworOD1V16klssMVnwr+TaofSKCo1Lp9flprtSTfCuyiiuZNHMMtVxmsGzde7I/yhZTJ648lsxRX0lRV9U+NeqN6+KwhawryZOXdZYcSOTkjefpA2kTuKSiSjZCyF/lx8x3XqHp6GApgU9FqWwa2v6lMFZUVctWQjkrfHl7yes0GxvCNW26sKa6WjbRnrUtljXVUo4Ps7EhvL3kdeaskPKUhxo4sKKqWva3mR5+ibUVpVxv7EiTkUG8GfAy81f5yfsfJRVV7DhFiuo6eeN51pXkMdzEiW29VzLNb7JschvUyp55pZWytdeor85y5bFkbjGV2mnyrHHMKal8ZMpAnVf6l4qiDP7E0kWvFbVcZrApVYr7bjh4ERPvlHGLqRO7LdjDmVuv0K+FFZcfuUHbN7fTV1gyt7SSeh5+DDVwkL0jL48ayoRp3rIHpNo6oryqWrbtDzt7U/bq7LXkANcbO/J8ZjGNvZYzYZq37D172NmNOSt8qefhx4LV87jfxkW2aw/UtuHMrVd4IrVADjPg9flpLm9px6bUc+w4ZS1PuvdhTbXkKVy+8X25o1fbdKvz3gbr27P/J8dls70dcTnyiLWutICOfpGMNO8qx6C5GfCyPNJvSjnL5S3tZH6p/SXU/Fp2KEnmV05JM36diZACtnkPYcI0bxoNX8aa/Wu43tiRrov3y/yymL7pJ/xaaySFeDb2Ws64KSPkUBZ10Vt556O3qefhx9zSSu63caGNT4TMr1c3X5Y9xhNy7+VXp6nredK9DzPfeYW67r4sW/ceI827SvwqzuW54Z40GxvCyIRchrR2YN+gY7IJZsS17Hv4pfZpqPh2meylLfNLlbDHwDOQSXkSv7r67+asbbEM1LaR+bXPsofMr5DWDnKoiIveQ3j9lRclfu0Nk/lVoeJX6Jl0fhGTQV8heaerR+P38Cs7SXYEVPMrT+VXYOMTIftevLr5Mnss3EsfWDI+p4zea85I/Eq7wE5T1/N4r94yv0rXLuLu9t1kfgVoWdNsbIjs+ftjfrV/KUwK4V5WdF9+RXv0ZYdJa2R+DQ49JYeEac6vJQcSaee7Q/Z/0O/nz6ylb8j8asyIlfl1JauE640dpVwJ1TWcrWHFkOg0fnXuXn6tadOFtYfX0dhrOa9N8pL9W+qiNsv+O4+ic24vdPi+ru1DFUUZ/InFXGhz0OooGngGMnbCC1yma8vcUinjlzomfEp+uTTCPpQkp2h0nruLYWdv8oB9T25q60jbN7fTYvomRpp35Q4zZ269mk0bnwg5NLU669iVrBL6wJJNaRdoMjJIjrd/xrM/jYYvY+//O8Jlura8PGooJ288zwGrTshx24d9Fs3WAwJYW17C93VtWVheJY1+8zM5W8OKOSt8qePmI/tGlKwJZLcFe+RcBmuNutDh7Z1yPoAtpk6MTMiVFZgPpCieWi4z5FhKTTeiaTY2hAm5ZXLseWOv5XRbepDZH8ziBa9BnB5+iS17z2aa32QGaFnTe80ZKZR0VSUX69iwtFKKr6PON5C19A3quPlw7g9xPN2/HwtWz+MzgXtpOjqYR7s/yzVtutDRL1IKo31xD8NNnORoo7VHN9IHlrISPpFawPXGjmyMO0Lz8aHs6r+boQYOPOneR86HcOejtxkzZCBnbYulrruvnDOhtqyIeh5+rKquYaC2Dauqa6jnIWVpU8fe13X35axtsYwZMpB3Pnpbjs9/0r0PQw0ceDVbytqljr56IrWAWi4z/hcfRxX24kCSlKWt4eIedpi0ho5+kXJeg+VHbvCZwL1y7oS5P8RJ8fVVeZ7riqWUqaWV1VysY8OaKklZjPrqLP01rWWLrVc3X+YFr0FcqWdHt6UHaey1XM4ZkZBbRrOxIWy6EU0fWMphs9VxqNR+LZEJudxi6sQ9nbqz45S1dHh7J9cadZFNW7st2MPisIX0FZKXsY6bDzOLKqTw0fmZNBy8iIXlUs6PuCkjqN/Pn8M+i5bzXgxYdYKTN57n5VFDubylnZz7oSE7icH69nJ+CXUoa3UomC9iJEfL6t3/pY1PhOxbEWnelRbTN8k5SQ7Y92TomXR5BuQrLNl48wq1XV+XI5uqZ1C5pZJzY+yEF9h6QAAHrY5i0swxTJo5hoNWR7H1gADGTniB7+va0uPDY5y59dHEJjIX2gzUtnmooiiDP7GYQJvVNTW0fXM7B6w6wcaE4wxqZS+F5j1yg7ruvjyfWSznc1XT6rr7yrRBrey57FCS5DF8q+T+tHFHGNRKMhdT09r57pBpr03yeiCtjpvPPbRxU0bItCuPJcu0jYlR96W18YmQaVfq2UlBzm5EU8fNhw5v75TDOcdNGcHTNwup6+7LoBMp96VVB2n7MW1h6PwH0iZM874/bcpZ9g06JtPubt+NCdO8GZUm0QZHpbJg9TyZ1uNDFW1F6T20RsOXybSzNaxk2i5zfmBtRSkjzbv+Iq2WywyZ1l9TCmnwE9rUcw+kbTN0CU1GBjF/lWQOesC+J59feVSiLS95aNrBoacemnbYZ9HcYeYsx+hX08bnSKGnDzm63pe28rsP2GboEpqODr6HtqSiSqatPx/58LQxO2XaBbviaTo6mAm5ZbzgNeiBtFVbVnDuD3E/oS0qr6Lla1vo9flp1sfs5PKWdvelPezsxqLyKnaeES7TLtO1lWm1XV9n4p0ynhvuSR9Y3kNbdybiZ2kLVbR6Hn7/o/3+IxoOXvRIOmczoc0ALeuHKk+bMhAk8bTCSlOX81tZ4hlnE7TYvAvfXMzCmYvZSDl5AEZW3WDR1QYn++Yha+dupOy5gdPZ5XjGUBeOQ6xwfu7nCD+TibRrOSi7dR0m9i5IDHRA9eGtSNlxFhv3pcJKTws97YxgtOcgvrucjaPnb+P6od1o09kJHZ3tcXZEDXJ/iETyrngcTinCM4a6eCH9Ig6ll2DT2UwcWbMWLY3MYeLQC0lLXdBw8nukbI/ClxGJsGiphV6dDdDx+HFExOVi79lbuLp7Oww6OSD5m1nQT4tG/u6d+Pd7+6GvqYFuBjoYf/syTmSW45tzmdi5+gvotDaGsZ0rUoL6o+nMDtyMOIxPv4lDB11NvJsSiVu6nfFDYj7emfN/EBot0LqDLeK/nY12BXEo2r0V783biZYtBBz0dfBq9gWczq2F97QP0FhbCa1Whjj87WL0MhZgzE4EeC1H/V3CXFcT713/HrmGDtibUoQ5vssBAK1MLRD73Tx0LE/FB07jcae2EdoaAkF7A6HRZywul2pg8D8/QENVGTR19bFr03vo21EPGpd2Y6FnIGqaCGPtFlgetwmFZi5wnhqKyjsZuNtYj/8LeRejncxg3ZCFj+xH4nZNA1oI4JMd/4KmxzgkNRrCY/IK1JYVoIV2S2xd/x48rQyx3cIV8eV1qGy8i/eWDofZyDGochgA+6mfozwrGXcb6xH4YQBe6tEBDi2KEWIzDBnV9QCAoPDXoTNwIrosu4bC5EuoKbkDz5mvYXofSwy3M8YRWzdcLatDcX0T3gkYgA6jR6Kp5z9gM20tSm8lwnGINzzdLfBP104oHzkcV1JLkFHdgJeH28B+XB/oD5sI56A05N+4guqiHDw/bRr++bwlvOxNcN7JHZdLa+HR0QB2XvawGOuNgTEdcTshAyWZ8bB5fgj6uHXEP3t1gsbUUYhPLEJyZR0meFjAYdyzMP7HRHT/LA93kq5Br21HWHazxIS+lnhh43yk7k/FpcJquBq1hN0IW1iNHY7hiU64GXcbJelX0alXf/Ts2R7T3DvDePYkxF/KRU0T0cOxLexH98S/7d/AmYvZyElMgLaeISy62uDFPp0xec9SpOxNwsWsCnRprY0ugyxhO3YgXsztg/T4PJSkx8K0Sy907dkem1rsQ8qOs7h+NgsFdU1wsW4D+5HdEfrcPBw9fxvZ128AADp1c8RQdwu8Gf3xPW3MyaMTUv69Ad/EZCIxNhcFSRdhbOcK2x7m2GV6Cinbo5By8hZSK+vRs2NrWJ46gecmf4jy4ysvkXT7PX2OudDhVM2OD0Ub3Hjzd1/vz8QTpQyEEC8AWA2gBaSkzx/+HH03kzb8R7kxTLQ1cbumAfV3iRYC6Gagi5zaBpQ13MXkAZ1RlFSMjKp6vBwyEQWxyciLzYH7lq9xN+MaquMu4Zb3QlgaamPkF+fgYtMWz1kZIeCTg2jT3gymnQxwbmsEdA1N0Mq0M/LiowAAmrr60Gqpj7qKYtxtrIfl8y+i4s4tBM57EefSipCWUYods59HanENLuWU4V+mOWgy74L8u3rA6rlo29MZOs7uODpqDkydTdCupw3WrziCznqaOF1UAwDQ19SAgaYGShqaUNNEjLE3RlZhNW7XNOJVv34oiM9CwfVCDNnxEeqTLiH/chJ03w2DcUMJ3jiUj+dsjNGzvQGmfxIFk44GcLQywva126HXtiMMzDsgPSoSLbRbQqe1EeoqStBUXwN7zzEozc1FdVE2Jr0xFvE3i1GYU4Fv5/XHldwynM8owWcD26BUtx0ySuvgdCoUus7uYCdnXJn2Cto9Y4ENn55GB11NdDZuiR/SS6CtIWCs3QKVjXdR2XgXANDfRA+3qhtQWN+E6eMdUZBQiOycCozf8DZKYuPR8ObHMNeqh1Z+MoJut4VbpzZwaNsS40LPoLOFITzsTfDhqkgYtLdCW/PWuLp7O7RbGUKvbQeUZMSDd5tg/ownqvJvoaYkD30mT0BhTgWKs/MRvGAYYm4W48rNYux+41lkljXg6p1yjK+MhoZtT9S06Yw7/tNg2tMBG+dvh3UrLZg4GCP81C0YarVAB11NJFbUoYlACwHYttJGXl0jKhvvYtJzHZGXXooXNy9ERfxVFFxJgdXn30M77wYaUy5jj9lw9DBrDQt9DYzaEItnrY3hbmmE2Z8cg3F7I7S3MMTJb7ehpZEZKnLSwLtNEBotYNDJAbUleaivKkN37/Eoyq1AZd5NzH57JGLSipB7uwzb5/bF9YJqXLhVgiUO1Wgw64LUmS+hXU87GPTogcipwTDrbAiz7qbYEB4PE21NWLfSwpH8KrQQkrxpawiUNdxF/V3Cu33re9pNflwu3L5bB96MRc31K8j0WgBLQ220KrsFv7N1eM7aGK7tDTAxOAomHVvD1aYtNny+46HaTU1JHv7xyhik3ypDYU45vvbvj0s5ZTiXVoRvRls9sN2Y9rDCho+OobOeJtqb6SPiegH0NTVgpNUChfWNcrsZ8sMqaHb1/N2ds5nQ4ZQWHR6KNqQp4zdfTwgxAcBSAE4A3ElefADdfftMIYQ1gC0A2gK4BGAayfqfveaTogyEEC0AJAMYCiALwAUAk0lef1Adt25deHTuBGR6LUAXQw2M+SYOJ7/dBoP2tihKvQyn4eNRcDMT//YfgeniKu526YvEGZOxMTIZFi21kFHdgH8OskLG1TyM/m4B8k9EQ8xbjXH/PQtX53bY8uUO1JYVoqm+Bp37eONr//44kJSP5d0acGrcLHTuZ431n59D/V2irOEuxjubIiW7AlM/nYSkgf9Ct3Yt4f3lBZzdvA365lYou5UI5xfGoiAjByv9h2NizRnAuT/iXp6GjftSYdFSS1Zqw81a4R/b3kPhyZOomxWE8atPw6WrGSK+3IaGmko01lbCuu9IbJzfH4eSC7DYtgInRvvCwqMz1q+7AttWWrhQUouJLmZIySjD1C+m49Kzb8DFXA8vrjmP899vR6t2FijPSkaPkRNwJzUHwQuGYV/8HXw11ARXpr0CiwFOWPfBIblRebdvjeE7P0DJqeMoe3k5JoScgrOjKfasi0BTYz0aqspgO3A0ijJSscBvJPzb56HWohduzZ6E9ZuuwUpPG/HltZj4bAekp5Zg4tdv4HTXqXA1b4WRYedwaUcE9Np2QFXBbdxtrEf7nkMQMs8Th5Ly8WlffVyc8ios+jlgXdBxlKsUyyhLQwzd+RFKTx1G/kvvY+KqU3BwNMGhDdvQur0tCpMvwGHwWBTeTMPi+SPxZut0NNj0RurMl7Dh+0RY6WkhsaIOU563QHpSISZsmoMj1mPh3kEfoz6LQZ+uZtjw+Q5UF+XgbmM9Oj47AmHz+uFAYj6C3TVxdvzrsBzogHUhp2RlN8beGEl3qjD9k3G45b0QXQyAMd8m3Fc23583Aq9pxqPJwQNJr03Ghh1JsmzW3yU8TfVk2dTw/xRjQ8/g2a5mCF+z/R7Z/HJePxy+UfAT2bRtpY2Y4hqMdzZFUlY5podORtLAf6G7qS68v76Es1u2o5WphSyb+em3sWK+FybXnwec+iFu2j/ReVA3bHhv731ls+HNjzHu09N4xqkdtq/djvqqclk21/v3w5GUQiy2rUCFWVcULJyOdV9deqBs9jTXg/ePZPcCgvAAAApjSURBVLO793jkpefi4/lDcSAhD18Oa4fYqS/DYoATNi4/hLy6/8nmsO1LUXLqBCpmrMCEkGh0czLFrnU70Lq9LcxsOuLycq9HogwmajycMgi9+7uUgROAuwDWAJh/P2Xwc32mEOJ7ADtIbhFCfAHgKsnPf/aij3udSl0A9AFwsNn3QACBP1envX1XWr62hb2WHODu63e4Us+O6f5TWX8nXc7aVZ8vxU5vTL/EfYlSjuTaSsnpZ7GODa9klbD/J8fZ/qUwTg+/xP02Ljza/VlWR66m6ehgDvssmkl5ZQzQsmbp2kVy8o7TNwvZmCBZ8tSWFTE4Soor323BHibNHCNnV+q2YA/tfHcwOCqV640deXnUUDmr1twf4lheJeWbrT3+LRPvSGkrTUcHszpyNf1aWHG/jQunh1+i0fBl7P/JcZ7x7M9t7ZyZv8qPnWeE023pQe5LlJ79ZsDLrM/PpK67r7Q5fUeK4tg8P7Tla1uY9/Ecvq9ry5ghA9k36Bg7TFrDVzdf5j7LHnKWL5ORQdRymcHkvHIGaFlLKRRV68cGnoFybtlNbR0ZdCKFeh5+7LFwLxNfG8VgfXsmzxrHrv675Zy2a426SO8hUdrc03KZwQrVszfPMW02NoTVO1fxsLMbD9j35NRvLtB8fCgHrDrB0/37cbGOjZybV9v1dTkTlg+kVKG67r5SCOXcVKb5TWZQKykSpsu7+2g1cxtzVkhOSeeGe9Ljw2M0HLxIdmCarWHFmv1r5GdPyZec8mZrWLGovIpvRVyV8upe3s8LXoMYbuLE5Udu0MYngs8E7uX1V15ksL6U37chO0nO9FVXLOVuaEo5y6Mp+dR2fZ1V1TUsDJ3PQG0bxueUceB/TtJsbAirIj7mIUdXHnJ0ZVXExzQbG8KB/zkpZ9wrDJ0v5yE+mpLPphTJrLSuOJdhZ29K5rvZSUydM5HB+va8/sqLfCZwL218Irj8yA2GmzjxgtcgNlzeT8PBkjNWkSpRT83BtUzJl1JRphdIzmWzNay4p1N3OSe3x4fHeG64J8NNnOR4Ui7v7pMzjqX5TZZzGX8Rk8G6AilM9fKWdjycLD175xnhLFg9j4t1bHi6fz8OWHWC5uNDOfWbCzxg35OHnd1YvXMVzcaGcNDqKDmbn6+wZIUqXWrrAQFsTIyiDyy51qiLnNe7q/9uJs8ax2B9eya+Noo9Fu6lnocftV1ffyRr+CbQpg8sH6o8iusBOAHA7df0mQAEgEIAmveje+L3DIQQ4wG8QPJ11fdpAJ4j+faP6N4A8IbqazcA8X/qjT55MIH04v/uUPig8ECNB/HBkqTp7/ljIcQB1f8/DHQB1Db7/iXJL3/l9U7gwTOD+/aZkJaXYkjaqc5bANhPstvPXUvz19zYkwAVM78EACHExadpg+aPgMIDCQofFB6o8UfygeQLj+q/hBBHAJjf56dFJCMf1XUeFk+SMsgGYNHseyfVOQUKFCj4y4HkkN/5Fw/qM4sAtBFCaJJsxEP2pRq/82YeJS4AsBdCWAshtAFMArDrMd+TAgUKFDypuG+fSWnt/ziA8Sq66QB+cabxxCgDlQZ7G8BBAIkAvieZ8AvVftX6218UCg8kKHxQeKDGU88HIcQYIUQWpM3fvUKIg6rzHYQQ+4Bf7DMXApgnhEiFZF769S9e80nZQFagQIECBY8PT8zMQIECBQoUPD4oykCBAgUKFDy9ykAI8YIQ4oYQIlUI8c7jvp8/EkKIDCFEnBAiVghxUXXOWAhxWAiRovo0Up0XQohPVXy5JoRwfbx3/9sghFgnhMgXQsQ3O/ern1kIMV1FnyKEmP44nuX34AF8WCqEyFbJQ6wQYkSz3wJVfLghhBje7PxT216EEBZCiONCiOtCiAQhxL9U5/928vCH4nF7Hv9Gr7wWANIA2ADQBnAVgPPjvq8/8HkzAJj86FwQgHdUx+8A+Eh1PALAfkheiL0BnHvc9/8bn7k/AFcA8b/1mQEYA0hXfRqpjo0e97M9Aj4sheSI9GNaZ1Vb0AFgrWojLZ729gKgPQBX1XFrSCEYnP+O8vBHlqd1ZuAOIJVkOqXgS1sAjHrM9/RnYxSAjarjjQBGNzu/iRJiINkbt38cN/h7QDIKQPGPTv/aZx4O4DDJYpIlAA4DeGROQ38GHsCHB2EUgC0k60jeBJAKqa081e2FZC7Jy6rjCkiWMx3xN5SHPxJPqzLoCOB2s+9ZqnN/VRDAISHEJVU4DgAwI5mrOr4DwEx1/Ffmza995r8yL95WLYGsUy+P4G/AByGEFYCeAM5BkYdHiqdVGfzd0JekKwAvAG8JIfo3/5HSHPhvZSP8d3zmZvgcgC0AFwC5AIIf7+38ORBC6APYDsCPZHnz3/7m8vBI8LQqg79V6AqS2arPfAA7IU3789TLP6rPfBX5X5k3v/aZ/5K8IJlHsonkXQBfQZIH4C/MByGEFiRFEE5yh+q0Ig+PEE+rMvjbhK4QQrQSQrRWHwMYBilS6y5IbubAve7muwC8rLKo6A2grNlU+mnHr33mgwCGCSGMVEspw1Tnnmr8aA9oDP4XuXcXgElCCB0hJTexB3AeT3l7EUIISB60iSRXNftJkYdHice9g/1bCySLgWRIVhKLHvf9/IHPaQPJ+uMqgAT1s0JyMT8KIAXAEQDGqvMCwGcqvsThAbHQn/QCYDOkJZAGSGu7r/2WZwbwKqSN1FQAMx73cz0iPnyjes5rkDq+9s3oF6n4cAOAV7PzT217AdAX0hLQNQCxqjLi7ygPf2RRwlEoUKBAgYKndplIgQIFChQ8QijKQIECBQoUKMpAgQIFChQoykCBAgUKFEBRBgoUKFCgAIoyUPAEQQjRpIrCGS+E2CaE0PuV9TsIISJUxy4/iuY58mmL1qlAwZ8JxbRUwRMDIUQlSX3VcTiAS7zXyejX/NcrkOzL336Et6hAwV8WysxAwZOKUwDsVDHrf1AFZYsRQvQAACHEgGbx/K8IIVoLIaxUswptAP8GMFH1+0QhxCtCiP+q6loJIY6p/vOoEKKz6vwGVRz8M0KIdCHE+AfenQIFfzEoykDBEwchhCakoHxxAJYBuEKyB4B3AWxSkc0H8BZJFwD9ANSo61MK07wEwFaSLiS3/ugSoQA2qv4zHMCnzX5rD8nj1RvAh4/62RQoeFKhKAMFTxJaCiFiAVwEcAtSPJq+kMIvgOQxAG2FEAYATgNYJYSYA6ANycZfcZ0+AL5THX+juoYaP5C8S/I6/hcSWYGCvzw0H/cNKFDQDDWqkb4MKUbZT0HyQyHEXkgxak6rUjzWPoJ7qGt++UfwfwoUPBVQZgYKnnScAvBPABBCDARQSLJcCGFLMo7kR5Cicjr+qF4FpBSJ98MZSJE7ofrvU4/8rhUoeMqgKAMFTzqWAuglhLgGaQ1fHbLYT7VZfA1SRM/9P6p3HICzegP5R7/NBjBDVXcagH/9YXevQMFTAsW0VIECBQoUKDMDBQoUKFCgKAMFChQoUABFGShQoECBAijKQIECBQoUQFEGChQoUKAAijJQoECBAgVQlIECBQoUKADw/9ZYxTwu02NEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#source :- https://www.tensorflow.org/text/tutorials/transformer\n",
    "def get_angles(pos, i, d_model):\n",
    "  angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n",
    "  return pos * angle_rates\n",
    "\n",
    "\n",
    "def positional_encoding(position, d_model):\n",
    "  angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n",
    "                          np.arange(d_model)[np.newaxis, :],\n",
    "                          d_model)\n",
    "\n",
    "  # apply sin to even indices in the array; 2i\n",
    "  angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n",
    "\n",
    "  # apply cos to odd indices in the array; 2i+1\n",
    "  angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n",
    "\n",
    "  pos_encoding = angle_rads[np.newaxis, ...]\n",
    "\n",
    "  return tf.cast(pos_encoding, dtype=tf.float32)  \n",
    "\n",
    "\n",
    "\n",
    "def positional_encoding(position, d_model):\n",
    "  angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n",
    "                          np.arange(d_model)[np.newaxis, :],\n",
    "                          d_model)\n",
    "\n",
    "  # apply sin to even indices in the array; 2i\n",
    "  angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n",
    "\n",
    "  # apply cos to odd indices in the array; 2i+1\n",
    "  angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n",
    "\n",
    "  pos_encoding = angle_rads[np.newaxis, ...]\n",
    "\n",
    "  return tf.cast(pos_encoding, dtype=tf.float32) \n",
    "\n",
    "\n",
    "n, d = 2048, 512\n",
    "pos_encoding = positional_encoding(n, d)\n",
    "print(pos_encoding.shape)\n",
    "pos_encoding = pos_encoding[0]\n",
    "\n",
    "# Juggle the dimensions for the plot\n",
    "pos_encoding = tf.reshape(pos_encoding, (n, d//2, 2))\n",
    "pos_encoding = tf.transpose(pos_encoding, (2, 1, 0))\n",
    "pos_encoding = tf.reshape(pos_encoding, (d, n))\n",
    "\n",
    "plt.pcolormesh(pos_encoding, cmap='RdBu')\n",
    "plt.ylabel('Depth')\n",
    "plt.xlabel('Position')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "TD38R8P2X3xZ"
   },
   "outputs": [],
   "source": [
    "#source :- https://www.tensorflow.org/text/tutorials/transformer\n",
    "def create_padding_mask(seq):\n",
    "  seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n",
    "\n",
    "  # add extra dimensions to add the padding\n",
    "  # to the attention logits.\n",
    "  return seq[:, tf.newaxis, tf.newaxis, :]  # (batch_size, 1, 1, seq_len)\n",
    "\n",
    "def create_look_ahead_mask(size):\n",
    "  mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)\n",
    "  return mask  # (seq_len, seq_len)  \n",
    "\n",
    "\n",
    "def scaled_dot_product_attention(q, k, v, mask):\n",
    "  \"\"\"Calculate the attention weights.\n",
    "  q, k, v must have matching leading dimensions.\n",
    "  k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.\n",
    "  The mask has different shapes depending on its type(padding or look ahead)\n",
    "  but it must be broadcastable for addition.\n",
    "\n",
    "  Args:\n",
    "    q: query shape == (..., seq_len_q, depth)\n",
    "    k: key shape == (..., seq_len_k, depth)\n",
    "    v: value shape == (..., seq_len_v, depth_v)\n",
    "    mask: Float tensor with shape broadcastable\n",
    "          to (..., seq_len_q, seq_len_k). Defaults to None.\n",
    "\n",
    "  Returns:\n",
    "    output, attention_weights\n",
    "  \"\"\"\n",
    "\n",
    "  matmul_qk = tf.matmul(q, k, transpose_b=True)  # (..., seq_len_q, seq_len_k)\n",
    "\n",
    "  # scale matmul_qk\n",
    "  dk = tf.cast(tf.shape(k)[-1], tf.float32)\n",
    "  scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n",
    "\n",
    "  # add the mask to the scaled tensor.\n",
    "  if mask is not None:\n",
    "    scaled_attention_logits += (mask * -1e9)\n",
    "\n",
    "  # softmax is normalized on the last axis (seq_len_k) so that the scores\n",
    "  # add up to 1.\n",
    "  attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)  # (..., seq_len_q, seq_len_k)\n",
    "\n",
    "  output = tf.matmul(attention_weights, v)  # (..., seq_len_q, depth_v)\n",
    "\n",
    "  return output, attention_weights\n",
    "\n",
    "\n",
    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "  def __init__(self, d_model, num_heads):\n",
    "    super(MultiHeadAttention, self).__init__()\n",
    "    self.num_heads = num_heads\n",
    "    self.d_model = d_model\n",
    "\n",
    "    assert d_model % self.num_heads == 0\n",
    "\n",
    "    self.depth = d_model // self.num_heads\n",
    "\n",
    "    self.wq = tf.keras.layers.Dense(d_model)\n",
    "    self.wk = tf.keras.layers.Dense(d_model)\n",
    "    self.wv = tf.keras.layers.Dense(d_model)\n",
    "\n",
    "    self.dense = tf.keras.layers.Dense(d_model)\n",
    "\n",
    "  def split_heads(self, x, batch_size):\n",
    "    \"\"\"Split the last dimension into (num_heads, depth).\n",
    "    Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)\n",
    "    \"\"\"\n",
    "    x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n",
    "    return tf.transpose(x, perm=[0, 2, 1, 3])\n",
    "\n",
    "  def call(self, v, k, q, mask):\n",
    "    batch_size = tf.shape(q)[0]\n",
    "\n",
    "    q = self.wq(q)  # (batch_size, seq_len, d_model)\n",
    "    k = self.wk(k)  # (batch_size, seq_len, d_model)\n",
    "    v = self.wv(v)  # (batch_size, seq_len, d_model)\n",
    "\n",
    "    q = self.split_heads(q, batch_size)  # (batch_size, num_heads, seq_len_q, depth)\n",
    "    k = self.split_heads(k, batch_size)  # (batch_size, num_heads, seq_len_k, depth)\n",
    "    v = self.split_heads(v, batch_size)  # (batch_size, num_heads, seq_len_v, depth)\n",
    "\n",
    "    # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\n",
    "    # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\n",
    "    scaled_attention, attention_weights = scaled_dot_product_attention(\n",
    "        q, k, v, mask)\n",
    "\n",
    "    scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])  # (batch_size, seq_len_q, num_heads, depth)\n",
    "\n",
    "    concat_attention = tf.reshape(scaled_attention,\n",
    "                                  (batch_size, -1, self.d_model))  # (batch_size, seq_len_q, d_model)\n",
    "\n",
    "    output = self.dense(concat_attention)  # (batch_size, seq_len_q, d_model)\n",
    "\n",
    "    return output, attention_weights\n",
    "\n",
    "\n",
    "def point_wise_feed_forward_network(d_model, dff):\n",
    "  return tf.keras.Sequential([\n",
    "      tf.keras.layers.Dense(dff, activation='relu'),  # (batch_size, seq_len, dff)\n",
    "      tf.keras.layers.Dense(d_model)  # (batch_size, seq_len, d_model)\n",
    "  ])\n",
    "\n",
    "\n",
    "\n",
    "class EncoderLayer(tf.keras.layers.Layer):\n",
    "  def __init__(self, d_model, num_heads, dff, rate=0.1):\n",
    "    super(EncoderLayer, self).__init__()\n",
    "\n",
    "    self.mha = MultiHeadAttention(d_model, num_heads)\n",
    "    self.ffn = point_wise_feed_forward_network(d_model, dff)\n",
    "\n",
    "    self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
    "    self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
    "\n",
    "    self.dropout1 = tf.keras.layers.Dropout(rate)\n",
    "    self.dropout2 = tf.keras.layers.Dropout(rate)\n",
    "\n",
    "  def call(self, x, training, mask):\n",
    "\n",
    "    attn_output, _ = self.mha(x, x, x, mask)  # (batch_size, input_seq_len, d_model)\n",
    "    attn_output = self.dropout1(attn_output, training=training)\n",
    "    out1 = self.layernorm1(x + attn_output)  # (batch_size, input_seq_len, d_model)\n",
    "\n",
    "    ffn_output = self.ffn(out1)  # (batch_size, input_seq_len, d_model)\n",
    "    ffn_output = self.dropout2(ffn_output, training=training)\n",
    "    out2 = self.layernorm2(out1 + ffn_output)  # (batch_size, input_seq_len, d_model)\n",
    "\n",
    "    return out2\n",
    "\n",
    "\n",
    "\n",
    "class DecoderLayer(tf.keras.layers.Layer):\n",
    "  def __init__(self, d_model, num_heads, dff, rate=0.1):\n",
    "    super(DecoderLayer, self).__init__()\n",
    "\n",
    "    self.mha1 = MultiHeadAttention(d_model, num_heads)\n",
    "    self.mha2 = MultiHeadAttention(d_model, num_heads)\n",
    "\n",
    "    self.ffn = point_wise_feed_forward_network(d_model, dff)\n",
    "\n",
    "    self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
    "    self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
    "    self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
    "\n",
    "    self.dropout1 = tf.keras.layers.Dropout(rate)\n",
    "    self.dropout2 = tf.keras.layers.Dropout(rate)\n",
    "    self.dropout3 = tf.keras.layers.Dropout(rate)\n",
    "\n",
    "  def call(self, x, enc_output, training,\n",
    "           look_ahead_mask, padding_mask):\n",
    "    # enc_output.shape == (batch_size, input_seq_len, d_model)\n",
    "\n",
    "    attn1, attn_weights_block1 = self.mha1(x, x, x, look_ahead_mask)  # (batch_size, target_seq_len, d_model)\n",
    "    attn1 = self.dropout1(attn1, training=training)\n",
    "    out1 = self.layernorm1(attn1 + x)\n",
    "\n",
    "    attn2, attn_weights_block2 = self.mha2(\n",
    "        enc_output, enc_output, out1, padding_mask)  # (batch_size, target_seq_len, d_model)\n",
    "    attn2 = self.dropout2(attn2, training=training)\n",
    "    out2 = self.layernorm2(attn2 + out1)  # (batch_size, target_seq_len, d_model)\n",
    "\n",
    "    ffn_output = self.ffn(out2)  # (batch_size, target_seq_len, d_model)\n",
    "    ffn_output = self.dropout3(ffn_output, training=training)\n",
    "    out3 = self.layernorm3(ffn_output + out2)  # (batch_size, target_seq_len, d_model)\n",
    "\n",
    "    return out3, attn_weights_block1, attn_weights_block2 \n",
    "\n",
    "\n",
    "from tensorflow.keras.layers import Input, Add\n",
    "class Encoder(tf.keras.layers.Layer):\n",
    "  def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,\n",
    "               maximum_position_encoding, rate=0.1):\n",
    "    super(Encoder, self).__init__()\n",
    "    \n",
    "\n",
    "    self.d_model = d_model\n",
    "    self.num_layers = num_layers\n",
    "\n",
    "    self.pos_encoding = positional_encoding(maximum_position_encoding,\n",
    "                                            self.d_model)\n",
    "\n",
    "    self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)\n",
    "                       for _ in range(num_layers)]\n",
    "\n",
    "    self.dropout = tf.keras.layers.Dropout(rate)\n",
    "\n",
    "  def call(self, x, training, mask):\n",
    "\n",
    "    seq_len = tf.shape(x)[1]\n",
    "\n",
    "    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
    "    x += self.pos_encoding[:, :seq_len, :]\n",
    "\n",
    "    x = self.dropout(x, training=training)\n",
    "\n",
    "    for i in range(self.num_layers):\n",
    "      x = self.enc_layers[i](x, training, mask)\n",
    "\n",
    "    return x  # (batch_size, input_seq_len, d_model)\n",
    "\n",
    "\n",
    "class Decoder(tf.keras.layers.Layer):\n",
    "  def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size,\n",
    "               maximum_position_encoding, rate=0.1):\n",
    "    super(Decoder, self).__init__()\n",
    "\n",
    "    self.d_model = d_model\n",
    "    self.num_layers = num_layers\n",
    "\n",
    "    self.embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)\n",
    "    self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)\n",
    "\n",
    "    self.dec_layers = [DecoderLayer(d_model, num_heads, dff, rate)\n",
    "                       for _ in range(num_layers)]\n",
    "    self.dropout = tf.keras.layers.Dropout(rate)\n",
    "\n",
    "  def call(self, x, enc_output, training,\n",
    "           look_ahead_mask, padding_mask):\n",
    "\n",
    "    seq_len = tf.shape(x)[1]\n",
    "    attention_weights = {}\n",
    "\n",
    "    x = self.embedding(x)  # (batch_size, target_seq_len, d_model)\n",
    "    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
    "    x += self.pos_encoding[:, :seq_len, :]\n",
    "\n",
    "    x = self.dropout(x, training=training)\n",
    "\n",
    "    for i in range(self.num_layers):\n",
    "      x, block1, block2 = self.dec_layers[i](x, enc_output, training,\n",
    "                                             look_ahead_mask, padding_mask)\n",
    "\n",
    "      attention_weights[f'decoder_layer{i+1}_block1'] = block1\n",
    "      attention_weights[f'decoder_layer{i+1}_block2'] = block2\n",
    "\n",
    "    # x.shape == (batch_size, target_seq_len, d_model)\n",
    "    return x, attention_weights\n",
    "\n",
    "\n",
    "\n",
    "class Transformer(tf.keras.Model):\n",
    "  def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,\n",
    "               target_vocab_size, pe_input, pe_target, rate=0.1):\n",
    "    super(Transformer, self).__init__()\n",
    "\n",
    "    self.tokenizer = Encoder(num_layers, d_model, num_heads, dff,\n",
    "                             input_vocab_size, pe_input, rate)\n",
    "\n",
    "    self.decoder = Decoder(num_layers, d_model, num_heads, dff,\n",
    "                           target_vocab_size, pe_target, rate)\n",
    "\n",
    "    self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n",
    "\n",
    "  def call(self, inp, tar, training, enc_padding_mask,\n",
    "           look_ahead_mask, dec_padding_mask):\n",
    "    \n",
    "    generated_images = generator(inp, training=True)\n",
    "\n",
    "    enc_output = self.tokenizer(generated_images, training, enc_padding_mask)  # (batch_size, inp_seq_len, d_model)\n",
    "\n",
    "    # dec_output.shape == (batch_size, tar_seq_len, d_model)\n",
    "    dec_output, attention_weights = self.decoder(tar, enc_output, training, look_ahead_mask, dec_padding_mask)\n",
    "\n",
    "    final_output = self.final_layer(dec_output)  # (batch_size, tar_seq_len, target_vocab_size)\n",
    "\n",
    "    return final_output, attention_weights\n",
    "    #return enc_output  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "yOe7hKGYYoW4",
    "outputId": "0ae2088e-4921-4baf-a4dc-2c46625c80d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Train Step')"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformer = Transformer(\n",
    "    num_layers=4, d_model=256, num_heads=8, dff=2048,\n",
    "    input_vocab_size=8500, target_vocab_size=40,\n",
    "    pe_input=10000, pe_target=6000)\n",
    "\n",
    "num_layers = 4      # number of layer used in model\n",
    "d_model = 256       # size of embedding dimension\n",
    "dff = 2048          # size of feed for network\n",
    "num_heads = 8       #size of multi heading \n",
    "dropout_rate = 0.1  #dropout rate\n",
    "\n",
    "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
    "  def __init__(self, d_model, warmup_steps=4000):\n",
    "    super(CustomSchedule, self).__init__()\n",
    "\n",
    "    self.d_model = d_model\n",
    "    self.d_model = tf.cast(self.d_model, tf.float32)\n",
    "\n",
    "    self.warmup_steps = warmup_steps\n",
    "\n",
    "  def __call__(self, step):\n",
    "    arg1 = tf.math.rsqrt(step)\n",
    "    arg2 = step * (self.warmup_steps ** -1.5)\n",
    "\n",
    "    return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)\n",
    "\n",
    "\n",
    "learning_rate = CustomSchedule(d_model)\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98,epsilon=1e-9)\n",
    "optimizer =  tf.keras.optimizers.RMSprop(learning_rate=0.01,rho=0.9,momentum=0.0,epsilon=1e-07)\n",
    "\n",
    "\n",
    "temp_learning_rate_schedule = CustomSchedule(d_model)\n",
    "\n",
    "plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))\n",
    "plt.ylabel(\"Learning Rate\")\n",
    "plt.xlabel(\"Train Step\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "hKgj8asjZkfC"
   },
   "outputs": [],
   "source": [
    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "    from_logits=True, reduction='none')\n",
    "\n",
    "\n",
    "def loss_function(real, pred):\n",
    "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
    "  loss_ = loss_object(real, pred)\n",
    "\n",
    "  mask = tf.cast(mask, dtype=loss_.dtype)\n",
    "  loss_ *= mask\n",
    "\n",
    "  return tf.reduce_sum(loss_)/tf.reduce_sum(mask)\n",
    "  return loss_\n",
    "\n",
    "\n",
    "def accuracy_function(real, pred):\n",
    "  real = tf.cast(real, dtype=tf.int64)\n",
    "  pred = tf.cast(pred, dtype=tf.int64)\n",
    "  accuracies = tf.equal(real, tf.argmax(pred, axis=2))\n",
    "\n",
    "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
    "  accuracies = tf.math.logical_and(mask, accuracies)\n",
    "\n",
    "  accuracies = tf.cast(accuracies, dtype=tf.float32)\n",
    "  mask = tf.cast(mask, dtype=tf.float32)\n",
    "  return tf.reduce_sum(accuracies)/tf.reduce_sum(mask)\n",
    "\n",
    "#source:- https://machinelearningmastery.com/beam-search-decoder-natural-language-processing/\n",
    "def beam_search_decoder(data, k):\n",
    "    sequences = [[list(), 0.0]]\n",
    "    # walk over each step in sequence\n",
    "    for row in data:\n",
    "        all_candidates = list()\n",
    "        # expand each current candidate\n",
    "        for i in range(len(sequences)):\n",
    "            seq, score = sequences[i]\n",
    "            for j in range(len(row)):\n",
    "                candidate = [seq + [j], score - log(row[j])]\n",
    "                all_candidates.append(candidate)\n",
    "        # order all candidates by score\n",
    "        ordered = sorted(all_candidates, key=lambda tup:tup[1])\n",
    "        # select k best\n",
    "        sequences = ordered[:k]\n",
    "    return sequences\n",
    "\n",
    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
    "train_accuracy = tf.keras.metrics.Mean(name='train_accuracy')\n",
    "\n",
    "def create_masks(tar):\n",
    "\n",
    "  # Used in the 1st attention block in the decoder.\n",
    "  # It is used to pad and mask future tokens in the input received by\n",
    "  # the decoder.\n",
    "  look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])\n",
    "  dec_target_padding_mask = create_padding_mask(tar)\n",
    "  combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)\n",
    "\n",
    "  return combined_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "A8gdn3EWZp1M"
   },
   "outputs": [],
   "source": [
    "trans_optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98,epsilon=1e-9)\n",
    "\n",
    "\n",
    "checkpoint_path = \"./checkpointzs/train\"\n",
    "ckpt = tf.train.Checkpoint(transformer=transformer,\n",
    "                           trans_optimizer=trans_optimizer)\n",
    "\n",
    "\n",
    "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n",
    "\n",
    "# if a checkpoint exists, restore the latest checkpoint.\n",
    "if ckpt_manager.latest_checkpoint:\n",
    "  ckpt.restore(ckpt_manager.latest_checkpoint)\n",
    "  print('Latest checkpoint restored!!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "LkjI10FTZzy8"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 400\n",
    "\n",
    "@tf.function\n",
    "def train_step(inp, tar):\n",
    "  tar_inp = tar[:, :-1]\n",
    "  tar_real = tar[:, 1:]\n",
    "\n",
    "  combined_mask = create_masks(tar_inp)\n",
    "\n",
    "  with tf.GradientTape() as tape:\n",
    "    predictions, _ = transformer(inp, tar_inp,\n",
    "                                 True,\n",
    "                                 None,\n",
    "                                 combined_mask,\n",
    "                                 None)\n",
    "    predictions = beam_search_decoder(predictions, 1)\n",
    "    loss = loss_function(tar_real, predictions)\n",
    "  gradients_trans = tape.gradient(loss, transformer.trainable_variables)\n",
    "  trans_optimizer.apply_gradients(zip(gradients_trans, transformer.trainable_variables))\n",
    "\n",
    "  train_loss(loss)\n",
    "  train_accuracy(accuracy_function(tar_real, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DGPsH6sG0zWI",
    "outputId": "3af07198-3567-4458-a02a-8a86267c2f49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 Loss 0.9811 Accuracy 0.1362\n",
      "Time taken for 1 epoch: 68.53 secs\n",
      "\n",
      "Epoch 2 Loss 0.6704 Accuracy 0.2428\n",
      "Time taken for 1 epoch: 25.18 secs\n",
      "\n",
      "Epoch 3 Loss 0.6426 Accuracy 0.2739\n",
      "Time taken for 1 epoch: 25.55 secs\n",
      "\n",
      "Epoch 4 Loss 0.6275 Accuracy 0.2879\n",
      "Time taken for 1 epoch: 24.80 secs\n",
      "\n",
      "Saving checkpoint for epoch 5 at ./checkpointz/train/ckpt-1\n",
      "Epoch 5 Loss 0.6267 Accuracy 0.2857\n",
      "Time taken for 1 epoch: 25.40 secs\n",
      "\n",
      "Epoch 6 Loss 0.6252 Accuracy 0.2858\n",
      "Time taken for 1 epoch: 24.82 secs\n",
      "\n",
      "Epoch 7 Loss 0.6386 Accuracy 0.2725\n",
      "Time taken for 1 epoch: 24.43 secs\n",
      "\n",
      "Epoch 8 Loss 0.6497 Accuracy 0.2590\n",
      "Time taken for 1 epoch: 24.50 secs\n",
      "\n",
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      "\n",
      "Saving checkpoint for epoch 10 at ./checkpointz/train/ckpt-2\n",
      "Epoch 10 Loss 0.6590 Accuracy 0.2512\n",
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      "\n",
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      "\n",
      "Saving checkpoint for epoch 15 at ./checkpointz/train/ckpt-3\n",
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      "\n",
      "Saving checkpoint for epoch 20 at ./checkpointz/train/ckpt-4\n",
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      "\n",
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      "\n",
      "Saving checkpoint for epoch 25 at ./checkpointz/train/ckpt-5\n",
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      "\n",
      "Saving checkpoint for epoch 30 at ./checkpointz/train/ckpt-6\n",
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      "\n",
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      "Saving checkpoint for epoch 40 at ./checkpointz/train/ckpt-8\n",
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      "\n",
      "Saving checkpoint for epoch 45 at ./checkpointz/train/ckpt-9\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Saving checkpoint for epoch 175 at ./checkpointz/train/ckpt-35\n",
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      "\n",
      "Saving checkpoint for epoch 180 at ./checkpointz/train/ckpt-36\n",
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      "\n",
      "Saving checkpoint for epoch 185 at ./checkpointz/train/ckpt-37\n",
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      "\n",
      "Saving checkpoint for epoch 190 at ./checkpointz/train/ckpt-38\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Saving checkpoint for epoch 335 at ./checkpointz/train/ckpt-67\n",
      "Epoch 335 Loss 0.3752 Accuracy 0.5146\n",
      "Time taken for 1 epoch: 24.47 secs\n",
      "\n",
      "Epoch 336 Loss 0.3757 Accuracy 0.5110\n",
      "Time taken for 1 epoch: 24.19 secs\n",
      "\n",
      "Epoch 337 Loss 0.3773 Accuracy 0.5106\n",
      "Time taken for 1 epoch: 23.88 secs\n",
      "\n",
      "Epoch 338 Loss 0.3758 Accuracy 0.5127\n",
      "Time taken for 1 epoch: 23.95 secs\n",
      "\n",
      "Epoch 339 Loss 0.3767 Accuracy 0.5107\n",
      "Time taken for 1 epoch: 23.85 secs\n",
      "\n",
      "Saving checkpoint for epoch 340 at ./checkpointz/train/ckpt-68\n",
      "Epoch 340 Loss 0.3773 Accuracy 0.5126\n",
      "Time taken for 1 epoch: 24.91 secs\n",
      "\n",
      "Epoch 341 Loss 0.3764 Accuracy 0.5102\n",
      "Time taken for 1 epoch: 23.98 secs\n",
      "\n",
      "Epoch 342 Loss 0.3761 Accuracy 0.5097\n",
      "Time taken for 1 epoch: 23.83 secs\n",
      "\n",
      "Epoch 343 Loss 0.3752 Accuracy 0.5116\n",
      "Time taken for 1 epoch: 23.90 secs\n",
      "\n",
      "Epoch 344 Loss 0.3740 Accuracy 0.5136\n",
      "Time taken for 1 epoch: 23.85 secs\n",
      "\n",
      "Saving checkpoint for epoch 345 at ./checkpointz/train/ckpt-69\n",
      "Epoch 345 Loss 0.3738 Accuracy 0.5142\n",
      "Time taken for 1 epoch: 24.56 secs\n",
      "\n",
      "Epoch 346 Loss 0.3748 Accuracy 0.5126\n",
      "Time taken for 1 epoch: 23.81 secs\n",
      "\n",
      "Epoch 347 Loss 0.3747 Accuracy 0.5133\n",
      "Time taken for 1 epoch: 23.85 secs\n",
      "\n",
      "Epoch 348 Loss 0.3745 Accuracy 0.5121\n",
      "Time taken for 1 epoch: 23.92 secs\n",
      "\n",
      "Epoch 349 Loss 0.3730 Accuracy 0.5151\n",
      "Time taken for 1 epoch: 23.87 secs\n",
      "\n",
      "Saving checkpoint for epoch 350 at ./checkpointz/train/ckpt-70\n",
      "Epoch 350 Loss 0.3752 Accuracy 0.5121\n",
      "Time taken for 1 epoch: 24.56 secs\n",
      "\n",
      "Epoch 351 Loss 0.3735 Accuracy 0.5142\n",
      "Time taken for 1 epoch: 24.05 secs\n",
      "\n",
      "Epoch 352 Loss 0.3736 Accuracy 0.5130\n",
      "Time taken for 1 epoch: 41.22 secs\n",
      "\n",
      "Epoch 353 Loss 0.3755 Accuracy 0.5128\n",
      "Time taken for 1 epoch: 23.84 secs\n",
      "\n",
      "Epoch 354 Loss 0.3740 Accuracy 0.5141\n",
      "Time taken for 1 epoch: 24.02 secs\n",
      "\n",
      "Saving checkpoint for epoch 355 at ./checkpointz/train/ckpt-71\n",
      "Epoch 355 Loss 0.3730 Accuracy 0.5131\n",
      "Time taken for 1 epoch: 24.67 secs\n",
      "\n",
      "Epoch 356 Loss 0.3744 Accuracy 0.5127\n",
      "Time taken for 1 epoch: 23.60 secs\n",
      "\n",
      "Epoch 357 Loss 0.3737 Accuracy 0.5115\n",
      "Time taken for 1 epoch: 23.40 secs\n",
      "\n",
      "Epoch 358 Loss 0.3735 Accuracy 0.5154\n",
      "Time taken for 1 epoch: 23.55 secs\n",
      "\n",
      "Epoch 359 Loss 0.3729 Accuracy 0.5147\n",
      "Time taken for 1 epoch: 23.57 secs\n",
      "\n",
      "Saving checkpoint for epoch 360 at ./checkpointz/train/ckpt-72\n",
      "Epoch 360 Loss 0.3746 Accuracy 0.5175\n",
      "Time taken for 1 epoch: 24.76 secs\n",
      "\n",
      "Epoch 361 Loss 0.3725 Accuracy 0.5161\n",
      "Time taken for 1 epoch: 23.79 secs\n",
      "\n",
      "Epoch 362 Loss 0.3737 Accuracy 0.5130\n",
      "Time taken for 1 epoch: 23.56 secs\n",
      "\n",
      "Epoch 363 Loss 0.3725 Accuracy 0.5161\n",
      "Time taken for 1 epoch: 23.33 secs\n",
      "\n",
      "Epoch 364 Loss 0.3735 Accuracy 0.5185\n",
      "Time taken for 1 epoch: 23.54 secs\n",
      "\n",
      "Saving checkpoint for epoch 365 at ./checkpointz/train/ckpt-73\n",
      "Epoch 365 Loss 0.3720 Accuracy 0.5168\n",
      "Time taken for 1 epoch: 24.83 secs\n",
      "\n",
      "Epoch 366 Loss 0.3732 Accuracy 0.5181\n",
      "Time taken for 1 epoch: 23.81 secs\n",
      "\n",
      "Epoch 367 Loss 0.3872 Accuracy 0.5043\n",
      "Time taken for 1 epoch: 23.58 secs\n",
      "\n",
      "Epoch 368 Loss 0.3732 Accuracy 0.5144\n",
      "Time taken for 1 epoch: 23.66 secs\n",
      "\n",
      "Epoch 369 Loss 0.3756 Accuracy 0.5140\n",
      "Time taken for 1 epoch: 23.63 secs\n",
      "\n",
      "Saving checkpoint for epoch 370 at ./checkpointz/train/ckpt-74\n",
      "Epoch 370 Loss 0.3814 Accuracy 0.5074\n",
      "Time taken for 1 epoch: 24.32 secs\n",
      "\n",
      "Epoch 371 Loss 0.3764 Accuracy 0.5113\n",
      "Time taken for 1 epoch: 23.81 secs\n",
      "\n",
      "Epoch 372 Loss 0.3733 Accuracy 0.5173\n",
      "Time taken for 1 epoch: 41.23 secs\n",
      "\n",
      "Epoch 373 Loss 0.3771 Accuracy 0.5142\n",
      "Time taken for 1 epoch: 23.71 secs\n",
      "\n",
      "Epoch 374 Loss 0.3732 Accuracy 0.5162\n",
      "Time taken for 1 epoch: 23.96 secs\n",
      "\n",
      "Saving checkpoint for epoch 375 at ./checkpointz/train/ckpt-75\n",
      "Epoch 375 Loss 0.3735 Accuracy 0.5185\n",
      "Time taken for 1 epoch: 24.69 secs\n",
      "\n",
      "Epoch 376 Loss 0.3740 Accuracy 0.5148\n",
      "Time taken for 1 epoch: 23.68 secs\n",
      "\n",
      "Epoch 377 Loss 0.3778 Accuracy 0.5195\n",
      "Time taken for 1 epoch: 23.39 secs\n",
      "\n",
      "Epoch 378 Loss 0.3726 Accuracy 0.5161\n",
      "Time taken for 1 epoch: 23.51 secs\n",
      "\n",
      "Epoch 379 Loss 0.3730 Accuracy 0.5171\n",
      "Time taken for 1 epoch: 23.57 secs\n",
      "\n",
      "Saving checkpoint for epoch 380 at ./checkpointz/train/ckpt-76\n",
      "Epoch 380 Loss 0.3717 Accuracy 0.5175\n",
      "Time taken for 1 epoch: 42.16 secs\n",
      "\n",
      "Epoch 381 Loss 0.3726 Accuracy 0.5175\n",
      "Time taken for 1 epoch: 23.97 secs\n",
      "\n",
      "Epoch 382 Loss 0.3717 Accuracy 0.5168\n",
      "Time taken for 1 epoch: 23.71 secs\n",
      "\n",
      "Epoch 383 Loss 0.3733 Accuracy 0.5203\n",
      "Time taken for 1 epoch: 23.34 secs\n",
      "\n",
      "Epoch 384 Loss 0.3714 Accuracy 0.5189\n",
      "Time taken for 1 epoch: 23.18 secs\n",
      "\n",
      "Saving checkpoint for epoch 385 at ./checkpointz/train/ckpt-77\n",
      "Epoch 385 Loss 0.3813 Accuracy 0.5181\n",
      "Time taken for 1 epoch: 24.03 secs\n",
      "\n",
      "Epoch 386 Loss 0.3713 Accuracy 0.5166\n",
      "Time taken for 1 epoch: 23.59 secs\n",
      "\n",
      "Epoch 387 Loss 0.3739 Accuracy 0.5167\n",
      "Time taken for 1 epoch: 23.27 secs\n",
      "\n",
      "Epoch 388 Loss 0.3729 Accuracy 0.5175\n",
      "Time taken for 1 epoch: 23.39 secs\n",
      "\n",
      "Epoch 389 Loss 0.3712 Accuracy 0.5181\n",
      "Time taken for 1 epoch: 23.36 secs\n",
      "\n",
      "Saving checkpoint for epoch 390 at ./checkpointz/train/ckpt-78\n",
      "Epoch 390 Loss 0.3718 Accuracy 0.5162\n",
      "Time taken for 1 epoch: 24.42 secs\n",
      "\n",
      "Epoch 391 Loss 0.3752 Accuracy 0.5185\n",
      "Time taken for 1 epoch: 23.45 secs\n",
      "\n",
      "Epoch 392 Loss 0.3719 Accuracy 0.5183\n",
      "Time taken for 1 epoch: 23.42 secs\n",
      "\n",
      "Epoch 393 Loss 0.3711 Accuracy 0.5167\n",
      "Time taken for 1 epoch: 23.39 secs\n",
      "\n",
      "Epoch 394 Loss 0.3720 Accuracy 0.5181\n",
      "Time taken for 1 epoch: 23.32 secs\n",
      "\n",
      "Saving checkpoint for epoch 395 at ./checkpointz/train/ckpt-79\n",
      "Epoch 395 Loss 0.3709 Accuracy 0.5183\n",
      "Time taken for 1 epoch: 24.03 secs\n",
      "\n",
      "Epoch 396 Loss 0.3736 Accuracy 0.5167\n",
      "Time taken for 1 epoch: 23.47 secs\n",
      "\n",
      "Epoch 397 Loss 0.3714 Accuracy 0.5190\n",
      "Time taken for 1 epoch: 23.29 secs\n",
      "\n",
      "Epoch 398 Loss 0.3715 Accuracy 0.5169\n",
      "Time taken for 1 epoch: 23.35 secs\n",
      "\n",
      "Epoch 399 Loss 0.3719 Accuracy 0.5193\n",
      "Time taken for 1 epoch: 23.30 secs\n",
      "\n",
      "Saving checkpoint for epoch 400 at ./checkpointz/train/ckpt-80\n",
      "Epoch 400 Loss 0.3723 Accuracy 0.5167\n",
      "Time taken for 1 epoch: 24.36 secs\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(EPOCHS):\n",
    "  start = time.time()\n",
    "\n",
    "  train_loss.reset_states()\n",
    "  train_accuracy.reset_states()\n",
    "\n",
    "  # inp -> image , tar -> character string\n",
    "  for (batch, (inp, tar)) in enumerate(train_batches):\n",
    "    train_step(inp, tar)\n",
    "\n",
    "  if (epoch + 1) % 5 == 0:\n",
    "    ckpt_save_path = ckpt_manager.save()\n",
    "    print(f'Saving checkpoint for epoch {epoch+1} at {ckpt_save_path}')\n",
    "\n",
    "  print(f'Epoch {epoch + 1} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}')\n",
    "\n",
    "  print(f'Time taken for 1 epoch: {time.time() - start:.2f} secs\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "N-EPJCUSN1ee"
   },
   "outputs": [],
   "source": [
    "#function to evaluate performance of model\n",
    "def evaluate(image, max_length=24):\n",
    "  #input image\n",
    "  encoder_input = image\n",
    "\n",
    "  li = [36] #passing 'Start' index \n",
    "  output = tf.convert_to_tensor([li])\n",
    "\n",
    "  for i in range(max_length):\n",
    "\n",
    "    output = tf.convert_to_tensor([li]) #converting into tensor\n",
    "    \n",
    "    #model predicting word character and atttention weight\n",
    "    predictions, attention_weights = transformer(encoder_input,output,False,None,None,None)\n",
    "    \n",
    "    # select the last word from the seq_len dimension\n",
    "    predictions = predictions[:, -1:, :]  # (batch_size, 1, vocab_size)\n",
    "\n",
    "    predicted_id = tf.argmax(predictions, axis=-1) #getting index from the list which has highest logit score\n",
    "\n",
    "    predicted_id = predicted_id.numpy()\n",
    "    li.append(predicted_id[0][0]) #appending the index to list\n",
    "    \n",
    "    #check if the predicted index is equal to index of 'End'\n",
    "    if predicted_id[0][0] == 39:\n",
    "      break\n",
    "\n",
    "\n",
    "  return li, attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "6RcpH7_PN_Nb"
   },
   "outputs": [],
   "source": [
    "# pedicting the corresponding word character for given image\n",
    "image  = lists[100]\n",
    "predict , attention_weights = evaluate(image, max_length=24)\n",
    "predictions = beam_search_decoder(predictions, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "_jrENbEYODJO"
   },
   "outputs": [],
   "source": [
    "k = t.word_index\n",
    "word_dict = {}\n",
    "for i , j in k.items():\n",
    "  j = str(j)\n",
    "  word_dict[j] = i\n",
    "\n",
    "# visualizing the image and its corresponding groundtruth word and predicted word\n",
    "def visualize(image , groundtruth , predicted):\n",
    "\n",
    "  #print image\n",
    "  plt.figure(figsize=(2, 2))\n",
    "  plt.title('Image')\n",
    "  plt.imshow(tf.keras.preprocessing.image.array_to_img(image[0]))\n",
    "  plt.axis('off') \n",
    "\n",
    "  #print Groundtruth word \n",
    "  string1 = [ word_dict[str(i)]  for i in groundtruth[1:-1]]\n",
    "  s = \"\"\n",
    "  s = s.join(string1)\n",
    "  print(f'The groundtruth word is : {s}')\n",
    "\n",
    "  #print predicted word \n",
    "  string2 = [ word_dict[str(i)]  for i in predicted[1:-1]]\n",
    "  s = \"\"\n",
    "  s = s.join(string2)\n",
    "  print(f'The predicted word is : {s}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gp42P0qQOHGC",
    "outputId": "76c6f0a2-80f6-45c6-c44e-6022d272b1a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The groundtruth word is : nilhtid\n"
     ]
    }
   ],
   "source": [
    "#print Groundtruth word \n",
    "string2 = [ word_dict[str(i)]  for i in predicted[1:-1]]\n",
    "s = \"\"\n",
    "s = s.join(string1)\n",
    "print(f'The groundtruth word is : {s}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "id": "ozGLmYDKOM4d",
    "outputId": "70532c29-b492-4784-872b-372137a7e45f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The groundtruth word is : night\n",
      "The predicted word is : nilhtid\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pedicting the corresponding word character for given image\n",
    "image = lists[100]\n",
    "actual_word = values[100]\n",
    "predicted , attention_weights = evaluate(image, max_length=24)\n",
    "visualize(image ,actual_word , predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "9MO3m-8vOQG5"
   },
   "outputs": [],
   "source": [
    "def plot_attention_head(attention ,h ,  string2):\n",
    "  fig = plt.figure(figsize=(20,20))\n",
    "  ax = fig.add_subplot(1, 1, 1)\n",
    "  i = h +1\n",
    "  head = 'Head ' + str(i)\n",
    "  plt.title(head)\n",
    "  ax.matshow(attention[h][0:], cmap='viridis')\n",
    "  fontdict = {'fontsize': 50}\n",
    "  ax.set_yticklabels([''] + string2, fontdict=fontdict, rotation=90)\n",
    "\n",
    "def plot(attention_weights,string2):\n",
    "  attention = attention_weights['decoder_layer1_block2']\n",
    "  attention = attention[0]\n",
    "  fig = plt.figure(figsize=(16, 8))\n",
    "  for h in range(8):\n",
    "    plot_attention_head(attention ,h , string2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "2kLYkzM6OS6I",
    "outputId": "b2b33fc8-fde6-4686-dff5-fcc6e78a4066"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Y-axis is the feature map dimension of image.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAMBwB6cuYCpHz6185JKzJun7oue/cZJ+19mNT79s6hImcfFPP2XqEiZx0ev8jq2Tsy6Z7vf74IRjbcr92pSfY1P+vD9yyWRdT+qs66fr++Lrp/scm/KzZF2/t6wr+9TVm3K/tq6m/M40pSl/x6b8DE2uPOUaM6EAAAAAGE4IBQAAAMBwQigAAAAAhhNCAQAAADCcEAoAAACA4Sa5Ol5V3SPJQ5KcP78d2kl73f2KZdQFAAAAwBgrC6Gq6tIkT0ryjUnuveTmhVAAAAAAu9jwEKqq7pXk55J888ZTS+6il9weAAAAAEs2NISqqi9N8oYkX5jj4dMyQ6NlB1oAAAAADDAshKqqc5K8Nsk9509tDp+ERwAAAABrZORMqB9Kcr/89fDpxiSvTvKWJO9N8skknxtYBwAAAAATGxlCPSXHA6hKcizJM5O8oLuPDuwXAAAAgF1mSAhVVQ/P7DC8ziyA6iRP6+4XjegPAAAAgN3twKB2Lz5h+R0CKAAAAID1NSqEunDT407yG4P6AQAAAGAPGBVCnT2/37gK3nsG9bOQqrqiqo5U1ZGjt906dTkAAAAAa2NUCPWJE5Y/M6ifhXT31d19uLsPHzzn3KnLAQAAAFgbo0KoG+b3G1fHu/BUGwIAAACw/40Kod6c5LZNy5cM6gcAAACAPWBICNXdn0nyq5mdE6qSPKaqzhzRFwAAAAC736iZUEny3MzOBdWZHY731IF9AQAAALCLDQuhuvt9SX44x6+Q99yqunxUfwAAAADsXiNnQqW7X5zkqsyCqDOT/G5VPWVknwAAAADsPkNDqCTp7mckeXJmh+bdJcnPVNW7q+pHquqyqrqwqs4YXQcAAAAA0zm4yMZVdccO++vMZkU9MMmPn9D2ttvs7oXeBwAAAACrtWh4s+2kaK7nt2W0BQAAAMAesZ0ZRH3nm6ysHUEWAAAAwB6wnRBK8AMAAADAQhYNoX5sSBUAAAAA7GsLhVDdLYQCAAAAYGFLvapcVf3opsUPd/fVS2z7iiRftLHc3c/ZSXvHzj6W2x9+247r2o6jX/sVk/S7zqb6WSfJfV96xmR9/8Erf2Gyvqf06Ld+79QlsEJT/n5PuT9f1/f99qf+7GR9P/px67lv+cCTdnpx5O179+Uvn6zvKT9L1vV7y5SmHOdT7lPv+dbbJ+t7SlP+vKf8HVvXcT6lKb+3TOmMq069bqkhVJJn5/gJx69PsrQQKsn3J/nyTcs7CqEAAAAAWJ0Dg9oddfLyGtg2AAAAAIOMCqFG6TvfBAAAAIDdZq+FUAAAAADsQXsphNp8Brejk1UBAAAAwML2Ugh1/qbHn5qsCgAAAAAWtidCqKq6a5J7b3rq5qlqAQAAAGBxeyKESvLEHL8qXid514S1AAAAALCgg4tsXFX3XWDzM6vqPjkeHi3ijCRfkOQBSR6b5J9mFj7V/P5PttEmAAAAABNZKIRK8v7MQqCtbIROXzbffhk2wqcNv7qkdgEAAABYgUVDqOT0ZzZtZwbUqfSm+9/v7rctsW0AAAAABttOCLXVTKjNwdOdzZhaVCV5R2aH5gEAAACwh+yVmVA3JHlZkhd396eX2C4AAAAAK7BoCLXVLKTKLCjaOIH4B5I8a5t1HU1yS5JPJHl7d39im+0AAAAAsAssFEJ19y9ttb6qXraxaZK/vLPtAQAAAFgPBwa0WVnuoXhLU1VXVNWRqjpy7JZbpy4HAAAAYG1s55xQW9k88+kDS257x7r76iRXJ8lZD7ho2SdOBwAAAOAUlhpCdbcr1wEAAADw14w4HA8AAAAAPo8QCgAAAIDhhFAAAAAADCeEAgAAAGA4IRQAAAAAwwmhAAAAABhOCAUAAADAcEIoAAAAAIYTQgEAAAAwnBAKAAAAgOGEUAAAAAAMJ4QCAAAAYLjq7qlrmMRd64L+ynr0JH3f+PTLJumXaVz0/DdO1rexxjpY19+xdX3f62rKn/eUjLXVW9exNiXjnFXx3YFVeedVV17X3YdPts5MKAAAAACGE0IBAAAAMJwQCgAAAIDhhFAAAAAADCeEAgAAAGA4IRQAAAAAwx1cZOOqeuTJnu/ua0932xFO1j8AAAAAu8dCIVSSa5L0Cc/1Kdo52bYjnKp/AAAAAHaJ7YY3NWhbAAAAAPah7YZQGzOcTidgGjkbSsAFAAAAsAdsJ4QyCwoAAACAhSwaQj1q0LYAAAAA7GMLhVDd/foR2wIAAACwvx2YugAAAAAA9j8hFAAAAADDbffqeHtSVV2R5IokuUvOmbgaAAAAgPWxVjOhuvvq7j7c3YcP5aypywEAAABYG2sVQgEAAAAwDSEUAAAAAMMJoQAAAAAYTggFAAAAwHBCKAAAAACGE0IBAAAAMJwQCgAAAIDhhFAAAAAADCeEAgAAAGA4IRQAAAAAwwmhAAAAABhOCAUAAADAcEIoAAAAAIY7OHUBwDgXPf+NU5cwiRufftnUJQCDTLlfs29ZPT/v9TLlv/m6fmcCWDUzoQAAAAAYTggFAAAAwHBCKAAAAACGO61zQlXVI0cXsh3dfe3UNQAAAABw5073xOTXJOmBdWxHx4nVAQAAAPaERUOcGlIFAAAAAPvaoiHUbpkNJQwDAAAA2EMWCaF2GvxsDrBOp61FtwcAAABglzrdEOpR22z/0iTPTnKX+XIl+Wxm55i6LskNST6Z5NYk5yY5P8lDk3xFksuTnJnjYdRtSX4syVu2WQsAAAAAEzmtEKq7X79ow1X1fUmel+SMzMKnm5M8N8nLu/sTp/H6uyV5QpJ/k+TuSc6Zt/cD3X31ovUAAAAAMJ0DIxqtqn+S5GczC7kqyZuSPLS7f+p0Aqgk6e6bu/uFmc2M+qP50weTvKSqHj+gbAAAAAAGWXoIVVX3yyyAqswOpTuS5Ou6+6Pbaa+7P5bkG+bt9LzdF1fV/ZdRLwAAAADjjZgJ9a8zO3QuSe5I8sTu/vROGpy//olJjmUWRJ2d5Jk7aRMAAACA1VlqCFVVZyX5zsyCok5ybXe/fRltz9u5JrOZUJXkH8/7AwAAAGCXW/ZMqEszu8rdht9bcvuv3fT47CRfueT2AQAAABhg2SHUQ+f3Nb//0JLbv/EU/Z2Wqrqiqo5U1ZHP5fYllgUAAADAVpYdQl1wwvLBJbe/UW+for8tdffV3X24uw8fiiP5AAAAAFZl2SHU505Yvs+S299ob2Om1WeX3D4AAAAAAyw7hPq/8/uNmUqPXXL7J7Z305LbBwAAAGCAZYdQ7970uJJ8VVUt5eTh83Yuy/GAK0n+bBltAwAAADDWUkOo7r4uyQc2Fuft/2JVLXTuphPNX/+LOX4YXpJ8cN4fAAAAALvcsmdCJckr8/lh0UOTvL6qHrydxqrqQUmumbfT87Y7ySt2ViYAAAAAqzIihPqJfP5sqE7ysCR/WlUvqKqHnE4jVfXgqnpBkj+dv35DJ/lgkquWVzIAAAAAIx1cdoPdfVtVfW+S30lyaNOqM5NcmeTKqrohyXVJ3pXkk0luTXJukvOTPCTJV2Q28yk5PqtqYxbU7Ume2N23Lbt2AAAAAMZYegiVJN39B1X1bUlek1kQtXEy8Y1A6ctyPGQ6mc2H821+7WeTfFt3v26J5QIAAAAw2IjD8ZIk3f3bSR6V2RXsNs9m2rjVFrfN22X+3LuSXN7dvzOqZgAAAADGGBZCJUl3vynJI5I8O8lHcjxkSj4/aDrxlk3bfjTJs5I8orvfPLJeAAAAAMYYcjjeZt19e5LnVNVPJPnWJI9J8lWZHY5XJ3tJkhuSvDnJf0vym919dHSdAAAAAIwzPITaMA+SXjO/parOTnJhkrslOS/JLUluTvKx7v70quoCAAAAYLyVhVAnmgdNH5jfAAAAANjHqrvvfKt9qKo+luQvdtDEPZJ8fEnlwFaMNVbFWGNVjDVWxVhjVYw1VsVYY1V2Mta+pLsvPNmKtQ2hdqqqjnT34anrYP8z1lgVY41VMdZYFWONVTHWWBVjjVUZNdaGXh0PAAAAABIhFAAAAAArIITavqunLoC1YayxKsYaq2KssSrGGqtirLEqxhqrMmSsOScUAAAAAMOZCQUAAADAcEIoAAAAAIYTQgEAAAAwnBAKAAAAgOGEUAAAAAAM9/8Bx9JI5HJayfEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'The Y-axis is the feature map dimension of image.')\n",
    "plot(attention_weights,string2)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "nlp_transformer_model1.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
